Using action research to develop an intervention to increase children’s adherence to physiotherapy for cystic fibrosis
Bibliographic record
Abstract
Background: An action research (AR) approach [1] was used to develop a theoretically informed intervention (a film and action plan) to improve home chest physiotherapy adherence in infants and young children with cystic fibrosis. AR is a participatory, iterative approach characterised by inquiry as a group activity and a partnership between researchers and participants. AR is particularly useful for understanding and resolving complex problems and consequently is increasingly used in developing and refining complex healthcare interventions [2]. Aim: This paper’s aim is to describe the use of AR and explore the suitability of an online medium for developing an audio-visual intervention to inform future intervention development. Methodological discussion: The AR approach involved three iterative phases: theoretical testing, development, and practical testing/ refinement of the intervention. This iterative approach is consistent with the revised MRC framework [3]. We used AR in a novel way, in online interaction. The intervention was co-developed from May 2014 to April 2015 with a specially-recruited online group of 14 parents and 8 clinicians in the United Kingdom. Barriers and solutions to adherence, parents’ preferences for the intervention content and format and for the feasibility study design were explored. Advantages of an online environment for extended AR interactions were its suitability for this geographically-dispersed population, reduced participant burden compared to focus groups, and the ease of sharing multimedia materials. Challenges included lack of researcher control over participants’ response time, a reduction in parents’ interactions over time, difficulties conveying complex information succinctly and in an accessible way, and limits imposed by the textual format of parents’ responses. Conclusion: Despite these challenges, action research can be done online rather than face-toface and the iterative nature of AR was ideally suited to this creative project which resulted in successful intervention development. Recommendations are made for future intervention development using online AR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".