Community assessment workshops: a group method for gathering client experiences of health services
Bibliographic record
Abstract
Community assessment workshops were developed to gather client experiences of primary health care services in Australia. Primary health care services are particularly concerned with working with disadvantaged populations, for whom traditional client survey methods such as written surveys may not be inclusive and accessible. Service staff at six Australian primary health care services, including two Aboriginal-specific services, invited participants to attend workshops in 2011-2012. Participants were offered transport, childcare and an interpreter, and provided with reimbursement for their time. Ten workshops were run with a total of 65 participants who accessed a variety of services and programmes. A mix of age and gender was achieved. The workshops yielded detailed qualitative data and quantitative rankings for nine service qualities: holistic, effective, efficient, culturally respectful, used by those most in need, responsive to the local community, increasing individual control, supports and empowers the community, and mix of treatment, prevention and promotion. Discussions were audio recorded and transcribed for qualitative analysis. The workshop approach succeeded in being (i) inclusive, reaching users from disadvantaged sections of the community; (ii) comprehensive, providing ratings and discussion that took account of the whole service; (iii) richly descriptive, with researchers able to generate detailed feedback; and (iv) more empowering than traditional client survey methods, by allowing more control to participants and greater benefits than surveys of individuals. The community assessment workshops are a method that could be widely applied to health service evaluation research where the goal is to reach disadvantaged communities and provide ratings and detailed analysis of the experience of users. The participants and the research benefited from the group approach, and the workshops provided valuable, actionable information to the health services. Recruitment of users, particularly those from culturally diverse backgrounds, remains one of the key challenges facing evaluators.
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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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".