Patient-Reported Experiences and Satisfaction with Rural Outreach Clinics in New South Wales, Australia: A Cross-Sectional Study
Bibliographic record
Abstract
Introduction: Many studies have been conducted on how physicians view outreach health services, yet few have explored how rural patients view these services. This study aimed to examine the patient experience and satisfaction with outreach health services in rural NSW, Australia and the factors associated with satisfaction. Methods: A cross-sectional study was conducted among patients who visited outreach health services between December 2020 and February 2021 across rural and remote New South Wales, Australia. Data on patient satisfaction were collected using a validated questionnaire. Both bivariate (chi-squared test) and multivariate analyses (logistic regression) were performed to identify the factors associated with the outcome variable (patient satisfaction). Results: A total of 207 participants were included in the study. The mean age of respondents was 58.6 years, and 50.2% were men. Ninety-three percent of all participants were satisfied with the outreach health services. Respectful behaviours of the outreach healthcare practitioners were significantly associated with the higher patient satisfaction attending outreach clinics. Conclusions: The current study demonstrated a high level of patient satisfaction regarding outreach health services in rural and remote NSW, Australia. Further, our study findings showed the importance of collecting data about patient satisfaction to strengthen outreach service quality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".