Evaluation and facilitation of intervention fidelity in community exercise programs through an adaptation of the TIDier framework
Bibliographic record
Abstract
BACKGROUND: Despite high quality evidence supporting multiple physical and cognitive benefits of community-based exercise for people after stroke, there is little understanding on how to facilitate uptake of these research findings to real-world programs. A common barrier is a lack of standardised training for community fitness instructors, which hampers the ability to train more instructors to deliver the program as it was designed. Scaling up program delivery, while maintaining program fidelity, is complex. The objective of this research is to explore novel use of the Template for Intervention Description and Replication (TIDier) framework to evaluate and support implementation fidelity of a community exercise program. METHODS: We embedded intervention fidelity evaluation into an inaugural training program for fitness instructors who were to deliver the Fitness and Mobility Exercise Program for stroke, which has established efficacy. The training program consisted of a face-to-face workshop followed by 3 worksite 'audit and feedback coaching cycles' provided over 3 iterations of the 12-week program offered over 1 year. A modified TIDIER checklist (with 2 additional criteria) was used within the training workshop to clarify the key 'active ingredients' that were required for program fidelity, and secondly as a basis for the audit and feedback process enabling the quantitative measurement of fidelity. Data were collected from audits of observed classes and from a survey provided by fitness instructors who implemented the program. RESULTS: We demonstrated the feasibility of the TIDier checklist to capture 14 essential items for implementation evaluation of a complex exercise intervention for people with chronic health conditions over 3 iterations of the program. Based on the audit tool, program fidelity was high and improved over time. Three content areas for workplace coaching (intensity monitoring, space, and educational tips) were identified from the audit tool and were addressed. CONCLUSION: Training of staff to deliver exercises to high need populations utilising workshops and workplace coaching that used the TIDier framework for training, onsite audit and feedback resulted in a high level of fidelity to the program principles. A novel checklist based on the TIDier framework was useful for embedding implementation fidelity in complex community-based interventions.
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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 | 0.318 | 0.263 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".