Doctor safety in Australian after-hours house-call medical services: the use of chaperones and chauffeurs
Bibliographic record
Abstract
Objectives: The use of escorts (chauffeurs and chaperones) while on duty in after-hours-house-call (AHHC) is one key protective option available to doctors in the service and has been linked to low burnout and increased satisfaction in AHHC. This study aims to explore the patterns of engagement of escorts in Australian AHHC. Method: A questionnaire-based, electronic survey of all 300 doctors involved in AHHC through the National Home Doctor Service (NHDS), Australia’s largest providers of the service. Results: A total of 168 valid responses (56.0%) were received. 60.8% of the doctors engaged escorts. Of the doctors that engage chauffeurs, three-quarters do so “all or most times”, while only one-quarter engage chaperones to the same degree of frequency. Hiring escorts is very popular among Brisbane (91.7%) and Sydney-based (88.2%) practitioners, but is unpopular in the City of Gold Coast (26.1%). There were moderate patronages in Adelaide (52.9%) and Melbourne (46.4%). Compared to females, males were more likely to drive themselves (OR 5.34; P=0.001; CI 2.08 to 13.74) and less likely to use chauffeurs (OR 0.19; p<0.001; CI 0.07 to 0.51). Doctors in legally recognized social unions (OR 0.24; p=0.03; CI 0.07 to 0.83) and those that have attained the postgraduate fellowships (OR 0.43; p=0.02; CI 0.21 to 0.87) were both less likely to work without escorts. Conclusion: More needs to be done to increase the engagement of escorts by doctors involved in the Australian AHHC, particularly given their proven benefits in the service. Future studies may be needed to fully explore the real reasons behind the significant associations identified in this study.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".