Building physician resilience.
Bibliographic record
Abstract
OBJECTIVE: To explore the dimensions of family physician resilience. DESIGN: Qualitative study using in-depth interviews with family physician peers. SETTING: Hamilton, Ont. PARTICIPANTS: Purposive sample of 17 family physicians. METHOD: An iterative process of face-to-face, in-depth interviews that were audiotaped and transcribed. The research team independently reviewed each interview for emergent themes with consensus reached through discussion and comparison. Themes were grouped into conceptual categories. MAIN FINDINGS: Four main aspects of physician resilience were identified: 1) attitudes and perspectives, which include valuing the physician role, maintaining interest, developing self-awareness, and accepting personal limitations; 2) balance and prioritization, which include setting limits, taking effective approaches to continuing professional development, and honouring the self;3) practice management style, which includes sound business management, having good staff, and using effective practice arrangements; and 4) supportive relations, which include positive personal relationships, effective professional relationships, and good communication. CONCLUSION: Resilience is a dynamic, evolving process of positive attitudes and effective strategies.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".