Measuring outcomes experienced by users of community services—The development of the community services outcomes tree and the associated survey template
Bibliographic record
Abstract
Abstract There continues to be growing pressure from human service organizations, funders, and beneficiaries to demonstrate a service’s actions are having a demonstrable benefit. This article reports on the method of development and the results of the initial piloting in Australia of an outcomes framework and form of measurement for the human services sector, taking a ‘whole of life approach’ in recognizing the ways in which life domains interrelate. The design was based on a review of outcomes-related literature, while also using an action research method involving service staff and a consumer advisory body. The result was the Community Services Outcomes Tree (CSOT) and a lean measurement tool in the form of a customizable survey template. We argue the CSOT has passed an initial grounded test in providing meaningful data for each service involved in piloting it and has value in guiding practice and providing a way of measuring and informing outcomes of services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".