Evaluating children's rehabilitation services: an application of a programme logic model
Bibliographic record
Abstract
OBJECTIVES: To apply a programme logic model to evaluate the effectiveness of a new therapy service for children with special needs who were in transition from pre-school to kindergarten. SETTING: A children's outpatient rehabilitation centre in Ontario, Canada. MAIN OUTCOMES: The short-term outcomes included parents' perceptions of the transition process itself and the information they required, the children's skill development for the transition to kindergarten, and parents' perceptions of services and satisfaction with resources. METHODS: A combination of quantitative methods [Goal Attainment Scaling (GAS), Measure of Processes of Care (MPOC), Client Satisfaction Questionnaire (CSQ)] and qualitative interviews were used to evaluate both the process ('Outputs') and outcomes ('Short-term objectives') of the new therapy service. RESULTS: The children involved in the evaluation met or exceeded goals that were set by therapists and parents. Parents' perceptions of, and satisfaction with, the new service were higher than the provincial average. Qualitative data from interviews with parents and service providers supported the findings from standardized measures, and provided suggestions for future service delivery. CONCLUSIONS: The programme logic model provided researchers and service providers a collaborative and systematic approach to conducting programme evaluation in a relatively short-time frame. It appears to be a useful option for evaluation of other children's services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".