The Calling‐Cost Paradox: When Identity‐Driven Motivation Becomes a Risk in Medical Training
Bibliographic record
Abstract
Physicians are often fueled by more than external rewards or professional mastery; many experience a deeply internalised sense that being a doctor is central to who they are. Self-determination theory (SDT) labels this identity-level drive 'integrated regulation'-a form of autonomous motivation typically viewed as protective and performance-enhancing. This viewpoint introduces the Calling-Cost Paradox: the proposition that the very physicians and trainees who appear most autonomously motivated may be uniquely vulnerable to burnout when learning and practice environments lack clear boundaries, reciprocal support, or psychologically need-nurturing cultures. Drawing on empirical work in motivational profiling, need-sacrifice and work-family conflict, as well as the author's dual perspective as an SDT scholar and practicing family physician, this article traces how integrated motivation can blur boundaries, amplify perfectionistic norms and lead high performers to self-sacrificial overextension. It argues that simply moving learners along the SDT continuum is insufficient. Medical programmes must also implement structural safeguards, such as duty-hour limits, reflective mentoring, team-based scheduling and boundary-setting norms, to 'protect the purposeful.' By naming and unpacking the Calling-Cost Paradox, this paper invites further research and urges educators to recognise that high engagement does not necessarily equate to low risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".