Validity of participants’ self-reported diagnosis for a work absence due to a mental health problem compared with physician-certified diagnosis for the same work absence among 709 Canadian workers
Bibliographic record
Abstract
OBJECTIVES: This study assesses the validity of a self-reported mental health problem (MHP) diagnosis as the reason for a work absence of 5 days or more compared with a physician-certified MHP diagnosis related to the same work absence. The potential modifying effect of absence duration on validity is also examined. METHODS: A total of 709 participants (1031 sickness absence episodes) were selected and interviewed. Total per cent agreement, Cohen's kappa, sensitivity and specificity values were calculated using the physician-certified MHP diagnosis related to a given work absence as the reference standard. Stratified analyses of total agreement, sensitivity and specificity values were also examined by duration of work absence (5-20 workdays,>20 workdays). RESULTS: Total agreement value for self-reported MHP was 90%. Cohen's kappa value was substantial (0.74). Sensitivity was 77% and specificity was 95%. Absences of more than 20 workdays had a better sensitivity than absences of shorter duration. A high specificity was observed for both short and longer absence episodes. CONCLUSION: This study showed high specificity and good sensitivity of self-reported MHP diagnosis compared with physician-certified MHP diagnosis for the same work absence. Absences of longer durations had a better sensitivity.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".