You’d better like where you’re going…

Faculty Life

You’d better like where you’re going because you’ll be there for a long time. 

In my roles as attending physician, scientist, mentor, and director of an institutional career development office, I spend a lot of time talking to students, trainees, and early career scientists and physicians. Each conversation challenges me to reflect on my own journey, noting parallels and departures. As I progress in my career and find myself on the brink of being a “senior” faculty member (the horror!), these reflections have uncovered a truth about adulthood – it’s long.  

Yes, I am a rising senior who has been working as a physician scientist for 20 years. In some ways, I feel like I am just getting started. New students, innovative ideas, unexpected collaborations, exciting findings, keep the job fresh while a growing manuscript portfolio, lengthening list of “not discussed” proposals on ERA Commons, and ever stronger reading glasses remind me that I’ve been at this for a while.  

In the last year or so I’ve started to notice an underlying current of urgency in career development conversations I’ve had. Aspiring and early career scientists and physicians are anxious to get where they are going, and for good reason. It does take a very long time to become a scientist or physician. I added it up for my kids the other day when they asked me how many years of “school” I had. Four years of college, four years of medical school, three years of residency, three years of fellowship, before finally becoming an Instructor (an “almost faculty” position). I felt old by the time I finished.  

Trainees and Early Career faculty I talk to are loathe to add more years to that training. And I was too. So often I hear, “The opportunity sounds good, but I really don’t want to do another year of training,” or “I’ve been training forever, I’m ready to be done!” But what I’m realizing now with the benefit of 20 years of hindsight and hopefully another 20-30 years (fingers crossed) ahead of me, is that training is short in relation to the rest of it. You don’t want to sabotage the long game by conceding to short term pressures.  

All my years of training helped me to land in a career that I thoroughly enjoy. One that is different every day. One that continually challenges and excites me. In order to get where I am today, I had to take that long path.  

I do fully acknowledge that financial, family, or other responsibilities often dictate timing, but that doesn’t change the basic fact that you will be doing your “real” job for an awfully long time, so you’d better enjoy it. I’ve wondered where I would be today if I had tried to rush things. Maybe in a place that was not so fulfilling.  

If you are a student, trainee, or early career physician or scientist and find that you are saying to yourself, “I just want to be done and get to [insert job here]!”. I’d encourage you to take a moment and ask yourself, “What are you running towards?” 

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Getting an Edge through Mentorship

Book Reviews / Mentoring

Traditional mentorship programs tend to be organized via a hierarchy, with mentees being assigned a mentor to advance mentees’ careers. Under imprecise rubrics like “peer mentoring” or “lateral mentoring,” recent attention has focused on how key mentorships happen outside of institutional programs. Deborah Heiser started an organization called The Mentor Project to promote relationships with mentees that grow out of an accomplished veteran’s innate desire to give back. In The Mentorship Edge, she proposes that organizations best enable mentoring relationships by facilitating connections rather than by focusing on career advancement.

Like other books about mentoring, this book is filled with stories about unique ways this group’s mentees and mentors have connected. Mentorship books seem to collect stories of mentors going the extra mile to help a mentee. That’s the fun part because these noteworthy aspects seem to be the norm in healthy mentoring relationships. We all want to go the extra mile to help someone taste success.

Two sections stood out to me in this book. First, as a researcher centered in psychology, she grounds mentorship in Erik Erikson’s leading theory of adult development in her introduction. There’s a lot to his theory, but his description of a later life phase of “generativity” overlaps with mentorship. Wanting to give back through socially meaningful activities is a completely normal part of being an adult. We all do it, and we don’t need to don a superhero cape to mentor. Heiser captures this generative impulse that we all possess to describe what mentorship should be about. I appreciate the philosophical grounding of mentorship as a universal human need.

Second, the chapter on Mentoring in the Workplace caught my interest. Individual stories of healthy relationships certainly move me, but I’m interested in learning how mentorship scales in different industries. Through true-to-life narratives, she breaks down how mentoring can work in industries as different as healthcare, the military, and the law. She demonstrates how a lateral generativity can help even in the most vertical of environments.

By way of critique, I dislike her use of the term “lateral mentoring.” First, she puts a trademark sign every time she uses it. This unconventional branding distracts from the main message of the book. Most technical terms do not use a trademark, especially in a scientific environment. It speaks of capitalism, not scientific collegiality. Second, she tries to distinguish it from “peer mentoring” by claiming that peer mentoring is more emotional. In the field of biomedical research, this claim is untrue. Peer mentoring in the literature refers to what she refers to as lateral mentoring. Instead of acknowledging different uses, she tries to steer us to her trademarked term. This use raises my cynicism.

Nonetheless, this book paints a picture of how mentoring relationships can abound around us, and how naturally they can form (or something related to the “generativity” idea). While this book contains limited quantitative data, abundant qualitative stories illustrate her key observation that mentoring relationships stem from the mentor’s human need for generativity. I appreciate that her vision for mentorship is not limited to a fixed, hierarchical program but a culture of human relationships where we all help one another. In my experience, the best mentoring relationships are unplanned but grounded in human care.

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Enhancing Productivity with Roam Research

Productivity

As a healthcare researcher and academician, my professional life feels like a labyrinth of ideas, data, and endless streams of literature I should be keeping up with. It’s a realm that demands meticulous organization and a strategic approach to information management. If you’re looking for a way to do that, consider Roam Research – a tool that has become my compass in this maze.

Why Roam Research? Imagine having a personal assistant who not only helps you jot down ideas but also intelligently links these ideas across a vast network of information. This is what Roam Research does for me. It’s more than just a note-taking application; it’s a central hub where my thoughts, readings, and research findings coalesce into a meaningful whole.

A Daily Ritual of Notes and Reflections: I begin every day with Roam Research. I capture fleeting thoughts, summarize new findings, and outline out the day’s agenda. It’s a ritual that brings order to the chaos. At the heart of the software is a database of bi-directional links. When you want to convert a word or phrase into a concept that can be linked, you simply put double square brackets around it & voila, it has its own page stored in the database, and every mention of that concept is linked there.

Project Pages: Here’s an example of how this is helpful to those of us in academic research. Each project is a universe of its own – with unique objectives, timelines, and tasks. I use Roam Research by creating a dedicated page for each project. This page becomes the central hub for all project-related notes, from brainstorming sessions to detailed plans, and is tagged with [[Project Todos]]. It’s a dynamic space that grows and evolves with the project, ensuring that all my thoughts and tasks are coherently organized and easily accessible.

But of course, I’m always working on multiple projects. I can go to the [[Project Todos]] page where I get a bird’s-eye view of all my projects and can survey everything at a glance. It aggregates all tasks from each project into one comprehensive list, providing a panoramic snapshot of my entire project landscape. This powerful overview helps me prioritize tasks and manage my time effectively, ensuring that no project is left behind.

Categorizing Tasks: To plan my day/week, I actually have several lists I review at the beginning of the day: [[Paper Todos]], [[Grant Todos]], [[Work Todos]], and [[Personal Todos]]. Each category is like a stream of thought, collected into a reservoir of tasks that I can dive into depending on my focus for the day. This categorization helps me maintain a balance between various responsibilities, ensuring that I devote attention to each aspect of my academic and personal life. I also have a [[WaitingFor]] List, which is a unique system I use to track dependencies – tasks that are on hold because they require input or action from someone else.

If you’re not convinced yet, here are a couple other hacks that make my work a lot easier:

  • Literature Review: Managing my scientific literature in Roam Research is akin to curating a personal library. Each paper, tagged with relevant concepts, becomes part of a searchable, interconnected database. This system has revolutionized how I access information make it readily available through a simple query.
  • Recalling Meeting Discussions & Thoughts: During meetings, when a crucial point about a study is discussed, I tag it with [[Limitations Section]], [[Methods Section]], or even [[Acknowledgements Section]]. This method ensures that when I’m ready to draft a manuscript, a simple query of [[Title of Paper]] and [[Limitations Section]] brings forth all the relevant discussions, neatly linked and ready for review.

Pricing: I wish I could say it’s free, but given all the bells and whistles, including its reliability, I see why they charge for it. Depending on how frequently you want to pay, it costs from $8.33/month (if you purchase the 5-year Believer Plan like I do) to $15/month (if you chose monthly).

Conclusion: Roam Research is more than just a tool for me. With Roam Research, my daily organization transcends mere to-do lists; it becomes a form of art. I have created structured yet flexible spaces for each aspect of my work and life. It’s not just about getting things done; it’s about knowing and navigating the landscape of my commitments. It’s an extension of my thought process, a way to navigate the complexities of academia, research, and even my personal life with greater efficiency. It has redefined how I organize my academic life, turning the potential chaos of information overload into a structured, manageable journey.

If you want to learn more, here are a couple 10-min reads written by others:

READ MORE

Tips for Conquering the Literature

Order from Chaos

How to Do It All by Not Doing It All

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Don’t Let Your Research Questions Go Out Without PICOTS

Doing Research / Grants & Funding

All the best aims are wearing PICOTS (pronounced “peacoats”). Specification of your PICOTS* is the minimum outerwear required to prevent your research question from being caught in a downpour of questions. Having these details tucked in gets you ready to have a meaningful conversation with colleagues, evaluate feasibility, brainstorm about how to get the best study done, and prepare to share your concept:

P = Population

I (E) = Intervention (Exposure)

C = Comparator

O = Outcome

T = Timing

S = Setting

Use PICOTS as a checklist for operationalizing research questions and probing how the research would shape up under different assumptions. Ask:

Population: 

  • What group of participants is ideal?
  • Whom does this imply we need to include or exclude?
  • How would we operationalize those criteria?
  • What influence will that have on ability to identify participants/recruit well?
  • Do we need to worry about proof of concept or generalizability more at this stage?

Intervention:

  • What will participants do/experience in the study that is being tested for its effects?
  • What dose, frequency, intensity will be tested?
  • Do we need to invoke a specific behavioral or causal model?

Or Exposure:

  • What is the behavior, biomarker, experience, metric for which we are interested in evaluating the effect?
  • How will it be measured?
  • How will we ensure quality of the measure?

Comparator:

  • What comparison provides the most relevant contrast (e.g. usual care, no intervention, placebo, etc)?
  • What analytic approach will best support the comparison?
  • Does this comparator help test our causal model or could it be stronger and more direct?

Outcome:

  • What is our measurable outcome?
  • How will measurement be operationalized?
  • Do we need primary and secondary outcomes?
  • Can we achieve adequate power to assess the outcome?
  • If there is loss to follow-up, do we have alternative ways of assessing outcomes?

Timing:

  • Over what time frame will participants be recruited?
  • What is the time period over which intervention will be conducted for an individual participant?
  • How long after completion of intervention will measures be collected?
  • When will outcomes measured? How wide is the tolerable window for measurement?

Setting:

  • Where will the research be conducted or participants be recruited (e.g. academic tertiary care center, network of health department clinics, community-based, etc.)?
  • What are the characteristics of that setting?
  • If extant data, what was the setting in which the data was developed?

Try it, you’ll like it. And it’s better than the alternative of getting soaked later by questions and requests for details needed to clarify your concept for the research.

Taking PICOTS for a Spin

For example, if you’re interested in asking: “Do community-based lifestyle interventions really work?” or “What determines who stays in community-based lifestyle interventions?” work the PICOTS:

Initial Question: “Do community-based lifestyle interventions really work?”

Goal: Pilot intervention study with a primary aim of determining if an intervention results in weight loss

In this case a pilot would be a typical approach for estimating the effect size, feasibility, participant satisfaction, loss to follow-up, and need for adjustments to inform design of a future definitive randomized trial. So we sketch a picture of what the study could look like:

P: Adult women with physician’s permission who are registered for the first session of the 12-week New Beginnings Program, and who speak English or Spanish.

I: Structured small group (n=5 to 8) coaching program with 1) specific weekly goal setting targets (eliminating sodas, understanding metabolic effects of exercise and tracking, counting carbohydrates, planning daily physical activity, enhancing sleep, writing an individual vision for one’s health, making a long term health contract with oneself, etc.) 2) three small group resistance and circuit training coached sessions each week, 3) social media peer connections, and 4) individualized exercise, diet and stress-reduction prescriptions.

C: Women who have applied for the program and are eligible but who are currently on the wait list with an anticipated wait time of 14 or more weeks.

O: Primary outcome will be weight loss, measured as difference between first measurement (in pounds to one decimal place on scale provided and calibrated by the study) at intake session and weight at the last group session. Outcomes will be grouped by completion status where completers attended ≥75% of schedule sessions and non-completers fewer sessions. Weight loss will also be described by group for each of the 12 weeks. For secular trend among those with an intention to lose weight the wait list comparison (secondary analysis) group weight will be collected from initial application (or as documented in physician’s permission letter in application) and weight at intake session, adjusted for elapsed time between application and start of program.

T: The intervention will last for 12 weeks of structured lifestyle and exercise coaching. Informal peer and social media networks established during the intervention will continue unsupervised after completion. Secondary outcome data will be collected at 3, 6, and 12 months after completion of the intervention.

S: Privately owned gym facility partnered with non-profit (501C3) to provide a comprehensive lifestyle intervention program to means-tested low income women, the majority of whom are age 40 and older, African American and weigh, on average, more than 200 pounds.

Initial Question: “What determines who stays in community-based lifestyle interventions?”

Goal: Observational study of whether baseline mental and physical health status, locus of control, and dispositional optimism are associated with completion of a community-based lifestyle intervention

P: Adult women with physician’s permission who are registered the 12-week New Beginnings Program which is a structured small group (n=5 to 8) coaching program with 1) specific weekly goal setting targets (eliminating sodas, understanding metabolic effects of exercise and tracking, counting carbohydrates, planning daily physical activity, enhancing sleep, writing an individual vision for one’s health, making a long term health contract with oneself, etc.) 2) three small group resistance and circuit training coached sessions each week, 3) social media peer connections, and 4) individualized exercise, diet and stress-reduction prescriptions.

E: Lower levels^ of physical and mental health as assessed by Short Form 36, lower self-efficacy (assessed by Generalized Self-efficacy Sale), and greater pessimism (assessed by the Revised Life Orientation Test) at baseline.

C: Higher levels^ of physical and mental health as assessed by Short Form 36, internal locus of control, and greater optimism at baseline incorporated into logistic regression models to assess association of characteristics with outcome.

^ Cut offs to be determined by distribution of traits in context of national normative reference data.

O: Program completers will be classified as those who attended ≥ 75% of scheduled sessions and non-completers fewer sessions. Will also capture week of attendance for secondary analysis in time-to-event analysis.

T: The assessment will be completed within 12 weeks.

S: Privately owned gym facility partnered with non-profit (501C3) to provide a comprehensive lifestyle intervention program to means-tested low income women, the majority of whom are age 40 and older, African American and weigh, on average, more than 200 pounds.

But I can’t possibly know these details when I first think the thought!?

True, but you can get much closer than you think. Start by daydreaming and then add parameters that are initially fantasy. The approach to shaping questions jumpstarts thinking that then leads to:

  • Productive generation and sifting of research ideas.
  • Greater focus for literature review.
  • Strategic thinking about multiple aspects of feasibility .
  • Weighing the best choices for measures of exposure, covariates, and outcomes.
  • Enhanced ability to rapidly gather input from others.

Related Posts:

Acing Your Observational Research Aims

All research proposals – grants, dissertations, internal funding – must ace the description of aims.  Many scientific questions are interesting.  Not all are useful.  You must persuade your readers that the proposed aims/hypotheses to be tested and the related analysis will fill gaps in scientific knowledge.

Don’t Crash on Approach

Getting the approach – the methods section of your grant –  fine-tuned is literally the heart of it all. You must land your science smoothly. Study section members know, and recent evidence confirms, your grant’s score is not an equal weighting of component scores. NIH criterion scores are for significance, innovation, approach, investigators, and environment.

* Gordon Guyatt initially described PICOTS in Guyatt G, Drummond R, Meade M, Cook D. The Evidence Based-Medicine Working Group Users’ Guides to the Medical Literature. 2nd edition. McGraw Hill; Chicago: 2008. Subsequently the framework became standard for formulating inclusion and exclusion criteria for conduct of systematic evidence reviews and meta-analyses of interventions.

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Don’t Crash on Approach

Grants & Funding

Getting the approach – the methods section of your grant –  fine-tuned is literally the heart of it all. You must land your science smoothly. Study section members know, and recent evidence confirms, your grant’s score is not an equal weighting of component scores. NIH criterion scores are for significance, innovation, approach, investigators, and environment.

No surprises here, approach has the highest weight. Reviewers care most if the scientific methods in are sound. For studies with human participants from case-cohort studies to clinical trials you must implement this flight checklist:

  • Brief overview of the study design/population (repeated as necessary if this changes across aims).
  • Summary/figure detailing the timing and sequence of data collection including biological specimens, interview data, exposure measures, and outcomes.
  • Succinct summary of inclusion and exclusion criteria for participants (and if needed the larger study from which participants are identified).
  • Flow diagram indicating how many individuals were, or are estimated to be, excluded. Provide reasons if you have an extant cohort.
  • Clear estimates or exact numbers (better) of how many individuals will be available or recruited for analysis in each aim.
  • Operational definitions for: 1) Main exposure/intervention; 2) Primary and secondary outcomes; and 3) Key candidate confounders
  • Text introducing measures in a logical order (e.g., order that data is collected or order of relevance to aims).
  • Summary of general data quality assessment (e.g., logic checks) and data cleaning steps.*
  • Information about how missing or incomplete data will be handled.*
  • Details of quality control approach for any measures (labs, surveys, etc.).*
  • Description of analytic approach including data preparation, models to be used, and how choices will be made for any analysis of effect modification and confounding for each aim, if applicable.*
  • Methods for how you will check for and handle any violations of model assumptions.
  • Specific delineation between primary analyses and secondary analyses.
  • Power calculations supplemented with a table or figure.
  • Summary of potential challenges and solutions if they are encountered.
  • Timeline for completion of the work.
  • Conclusion/summary of the strength of the approach with a final pitch covering why the science is innovative.

Work the checklist. The glidepath provided by crisp and clear operational details will bring you in. A sound approach is required for a smooth landing.

* These items, in part, speak to the requirement to describe what aspects support rigor and reproducibility.

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