A Measure of Daily Distress in Practising Medicine
Bibliographic record
Abstract
OBJECTIVE: Existing measures of stress either focus on burnout or frustration and fatigue factors, often referred to as job strain. The objectives of this study were to: establish a reliable measure of distress that is sensitive enough to identify job strain at lower levels of distress and risk of burnout at higher levels of distress; and document levels of distress among the major medical specialties and across varying patterns of clinical practice. METHODS: A stratified cross-sectional survey of physicians in Canada was conducted in 2004. Among the eligible population, 2810 physicians (56.7%) responded. Response bias was negligible. Responding physicians completed a 13-item measure of distress. Confirmatory factor analysis was used to establish the measure. Scheffe tests were used to document differences in the levels of distress among specializations and by clinical practice profile. RESULTS: Factor analysis revealed reliable dimensions of: fatigue (alpha = 0.75) and reaction (alpha = 0.73). The distress measure was reliable (alpha = 0.82). Emergency physicians (n = 4.51), surgeons (n = 4.35), and general practitioners (n = 4.33) reported the highest levels of distress, while administrative physicians (n = 3.30), community health (n = 3.35), and clinical specialists (n = 3.46) reported the lowest levels of distress. Physicians with clinical and administrative responsibilities reported the highest levels of distress (n = 4.40), compared with purely clinical physicians (n = 3.94) and clinician-academics (n = 3.98). CONCLUSIONS: Some specializations are associated with more distress than others. Administrative duties appear to add to distress for all physicians. Counterintuitively, adding academic as well as administrative responsibilities appears to add less distress than adding administrative duties alone. Academic duties are viewed as advancing medicine.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".