Exploring the construct of anticipatory stress in finding a job after residency training through cognitive interviewing: Implications for learner well-being and health workforce planning
Bibliographic record
Abstract
Background: Anticipatory stress (AS) is denoted by concern about future events for which there is little control. Most AS research has been physiological studies such as measuring salivary cortisol levels. Medical learners may experience AS regarding employment after residency, however anticipatory stress AS a psychological construct across career stages has not previously been studied. The objective of this study is to explore the psychological construct of employment AS in medical students, residents, and former Program Directors (PDs). Methods: Participants were recruited from a large Canadian medical school via purposive sampling. Semi-structured interviews with n=21 participants (six medical students, nine residents, and six PDs) were transcribed verbatim, and coded by two independent reviewers using thematic analysis. Results: Participants agreed that financial, family, and geographical factors exacerbate AS, and it is mitigated by flexibility, social support, and being proactive. External support, job market saturation, and differences between medical specialties also influence AS. Perspectives unique to participant groups included: medical students reflecting on a hidden curriculum and preoccupation with proximal issues over distal concerns of employment; residents experiencing competing residency program demands; former PDs finding that resident competency, yearly hiring fluctuations, and existing stress impact AS. Consequences of AS include physical and psychological manifestations, performance anxiety, and pursuing additional training. Conclusions: Perceptions of AS vary by medical career stage. Individual, program and systems-level changes can help manage and address the underlying cause of AS: an unreliable job market for physicians. Correcting the mismatch between residency positions and job openings may be a proactive, preventative approach.
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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 | 0.039 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".