Profile of patients presenting to Australian osteopaths: Results from a national health service use survey
Bibliographic record
Abstract
Musculoskeletal conditions are one of the most common complaints affecting the Australian population. Affected individuals often seek care from a range of health professionals including osteopaths. Osteopaths provide care for musculoskeletal conditions using manual therapy, exercise, and patient education. The current study is a secondary analysis of data from an Australian health service use survey. Respondents were asked to indicate the range of health professionals they consulted during the period February 2021 and February 2022. Additional data collected from respondents related to demographic variables, presence of chronic conditions in addition to the Personal Wellbeing Index (PWI) and Short Form 20 (SF-20). Data were descriptively analysed based on whether the respondent reported consulting with an osteopath or not. In the period February 2021-February 2022, of the 2354 respondents, 143 (6.1%) indicated they had consulted with an osteopath. Over half of those who consulted with an osteopath identified as female (51.7%), had private health insurance (59.4%) and/or a healthcare card (69.9%). No significant difference was observed between respondents who consulted with an osteopath and those who did not for PWI scores (p>0.05). SF-20 scores were significantly lower for those who consulted with an osteopath compared with those who did not (p<0.01). This secondary analysis contributes to our understanding of the profile of patients presenting to Australian osteopaths, particularly health related quality of life and subjective wellbeing of this patient cohort.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".