Development of a fidelity measure for community integration programmes for people with acquired brain injury
Bibliographic record
Abstract
OBJECTIVE: The paper describes development of the Assessment of Community Integration Programme Attributes (ACIPA) measure based on a descriptive community integration framework. The purpose of this measure is to allow evaluation of community integration programmes for adults with acquired brain injury (ABI). METHODS: The Community Integration Framework (CIF) was used to design a fidelity evaluation measure through consultation with 37 participants from five stakeholder groups (practitioners, researchers, policy-makers, people with ABI and family members of people with ABI) using semi-structured interviews, focus groups, iterative surveys and a multi-attribute utility (MAU) method. RESULTS: The resultant measure included seven themes and 21 attributes. Each attribute included indicators and probing questions. Weights were assigned to each theme and constituent attributes. CONCLUSION: Programme evaluation commonly focuses on outcomes, often overlooking analysis of programme processes. Although it requires further psychometric (reliability and validity) development, the Assessment of Community Integration Programme Attributes may be used to assess the relationship between programme processes and specific outcomes and also to inform the development of programmes aiming to enhance community integration for adults with ABI.
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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 | 0.030 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".