When Basic Psychological Needs Are Turned Against Each Other
Bibliographic record
Abstract
To the Editor: We read Vaa Stelling and colleagues’ article1 with interest and commend their focus on system-level factors hindering professional identity formation (PIF) and physician well-being. To advance the discussion, we would like to raise 3 issues. First, it is critical that, in our collective efforts to promote physician well-being, we employ the term “impostor phenomenon,” as originally coined by Clance and Imes,2 instead of “imposter syndrome.” Besides the fact that inconsistent terminology weakens our ability to synthesize and extend the literature, using the word “syndrome” medicalizes a ubiquitous experience and adds to the stigma that physicians already face around wellness in medicine. Second, the authors employ self-determination theory to unpack the process of PIF, impostor phenomenon, and burnout, which early-career physicians often experience when transitioning from training to unsupervised practice. While this approach is not necessarily novel and has been used in various studies on PIF, impostorism, and burnout in health professions education,3,4 the findings further reinforce the notion that tensions to “fit in while standing out” start early on in medicine and continue into residency and early practice. Hence, system-level approaches to supporting physicians’ PIF and well-being should be built in early on, at the undergraduate level. Third, in self-determination theory, relatedness is our human need not to “fit in” per se (since this often requires us to sacrifice or betray part of who we are to gain approval and social status), but rather to feel genuinely connected to and valued by others. Autonomy is our need for volition versus feeling controlled.5 The problem is that the culture in medicine does not support individuality and self-determination. It rewards conformity and performance, which turns basic psychological needs against each another (i.e., relatedness with “fitting in” vs autonomy and competence with being oneself and “standing out”). This kind of contingent regard, and the dissonance it creates, invariably gives rise to feelings of inauthenticity, self-esteem fragility, stress, and burnout.5 We thus wholeheartedly agree with the authors that addressing structural and cultural factors is key, starting with institutional autonomy support for physicians and trainees. Adam Neufeld, MSc, MDFaculty, Family Medicine, University of CalgaryCumming School of Medicine, Calgary, Alberta,Canada; email: [email protected]; ORCID:http://orcid.org/0000-0003-2848-8100Oksana Babenko, PhDAssociate professor, Department of Family Medicine,Medical Education Research, University of Alberta,Edmonton, Alberta, Canada; ORCID:http://orcid.org/0000-0003-2140-1551.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.020 | 0.029 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".