Developing a framework for understanding doctors’ health access: a qualitative study of Australian GPs
Bibliographic record
Abstract
Health access behaviours of doctors need to be understood if the profession is to adequately respond to concerns raised about doctors' health. There has been limited investigation of these issues and most qualitative studies have focussed on doctors who have been seriously unwell. This research project was designed to explore doctors' attitudes to health access and the barriers they experience using six independently facilitated focus groups (37 general practitioners) in Brisbane, Australia. Themes that emerged using inductive thematic analysis were grouped into three key categories. The findings challenge current representations of doctors' health within the medical literature. Doctors in this study reported positive attitudes towards their own health care. Health access, however, was difficult because of the barriers they encountered. These barriers are described in detail revealing the rationale used by doctors seeking care. A framework of patient, provider and profession barrier domains is developed to enable a comparison between the health access barriers of the doctor and those experienced by the general community. The complexity is highlighted as the socio-cultural factors woven through these barrier domains are recognised. The potential for this framework to provide a structure for future interventions to enhance doctors' health access is discussed.
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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 | 0.037 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".