Utility of Logic Models to Plan Quality of Life Outcome Evaluations
Bibliographic record
Abstract
Abstract Quality of life is widely accepted as an important concept in the evaluation of health and social services provided to persons with intellectual disabilities. While quality of life has been studied as a service outcome and measure of program improvement, its application to multiple levels of program delivery and evaluation remain unclear and can be difficult for community‐based agencies that lack resources. An approach using program logic models and including program staff can build evaluation capacity. Logic models can be used to link service components with relevant quality of life outcomes at short‐term, intermediate, and long‐term points in service delivery. The models can then guide the development of evaluation plans. A case example of how this approach is being used at a service agency in Toronto, Canada, is described. An explanation of how an agenda for quality of life program evaluation developed within the agency is provided, and links between service activities and quality of life outcomes are described. The integration of program logic models into an expanded organizational model defines how quality of life data can influence decision making about programs at the service, organizational, and system levels.
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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 | 0.024 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".