Optimal communication from occupational physicians to GPs: a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Correspondence from occupational physicians to GPs is infrequent, despite evidence that good communication leads to earlier return to work of sick-listed patients and is cost effective. AIM: To explore the circumstances, content, and preferred method of communication GPs would value from an occupational physician, following an occupational health consultation with one of their patients. DESIGN AND SETTING: A cross-sectional survey in the UK. METHOD: A questionnaire was developed de novo, piloted, and sent to 600 GPs of consecutive employees undergoing occupational physician assessments. Descriptive data were generated using Excel. RESULTS: The response rate was 374/600 (62%). Demographic features of GP responders reflected national figures. A total of 372 (99.5%) GPs wanted information from occupational physicians. Most wanted information on diagnosis (303, 81%), clinical assessment (275, 74%), functional assessment (295, 79%), or advice on the timing (308, 82%) and adjustments 290 (78%) of any return-to-work plan. Over 80% wanted information following every occupational physician consultation, and over 90% wanted information on the timing of a return to work, adjustments suggested, or if different medical diagnosis or management was suggested. The preferred method of communication was letter by post 341/374 (92%). Brief, relevant information was valued and considered useful for completing 'fit notes'. CONCLUSION: Occupational physicians should send formal letters, by post, to the patient's GP following occupational health assessments. This would assist the GP in completing the patient's 'fit note' and ultimately increase the chances of their patient being rehabilitated back to work.
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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 | 0.002 | 0.012 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".