Theory-Based Programme Development and Evaluation in Physiotherapy
Bibliographic record
Abstract
PURPOSE: Programme evaluation has been defined as "the systematic process of collecting credible information for timely decision making about a particular program." Where possible, findings are used to develop, revise, and improve programmes. Theory-based programme development and evaluation provides a comprehensive approach to programme evaluation. SUMMARY OF KEY POINTS: In order to obtain meaningful information from evaluation activities, relevant programme components need to be understood. Theory-based programme development and evaluation starts with a comprehensive description of the programme. A useful tool to describe a programme is the Sidani and Braden Model of Program Theory, consisting of six programme components: problem definition, critical inputs, mediating factors, expected outcomes, extraneous factors, and implementation issues. Articulation of these key components may guide physiotherapy programme implementation and delivery and assist in the development of key evaluation questions and methodologies. Using this approach leads to a better understanding of client needs, programme processes, and programme outcomes and can help to identify barriers to and enablers of successful implementation. Two specific examples, representing public and private sectors, will illustrate the application of this approach to clinical practice. CONCLUSIONS: Theory-based programme development helps clinicians, administrators, and researchers develop an understanding of who benefits the most from which types of programmes and facilitates the implementation of processes to improve programmes.
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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 | 0.278 | 0.328 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".