Professionals’ Perceptions on Implementing an Adapted Lifestyle Coaching Program for People with Physical Disabilities
Bibliographic record
Abstract
Background/Objectives: Evidence-based lifestyle coaching programs have been developed to support people with disabilities in adopting healthy behaviors, and to ultimately contribute to enhancing their overall well-being. However, when implementing such programs in new settings, adaptations may be needed to ensure a successful implementation process. This study aimed to explore professionals’ perceptions on an adapted evidence-informed lifestyle coaching program (Healthy Habits Coaching) for people with physical disabilities to inform the implementation of the program in Dutch rehabilitation and/or community settings. Methods: A qualitative study with semi-structured interviews was conducted. The study was performed from a pragmatic perspective using an integrated knowledge translation approach. Ten professionals who had experience with offering, delivering, and/or implementing lifestyle coaching programs were enrolled. Interview questions focused on participants’ perceptions on implementing the Healthy Habits Coaching in Dutch settings. A directed content analysis was used to analyze the data. Results: Participants highlighted the importance of implementing lifestyle coaching tailored to people with physical disabilities. While participants were generally positive about the implementation of Healthy Habits Coaching, they had mixed opinions on its added value alongside existing lifestyle programs and on the core components, particularly the free coaching model and the use of volunteer coaches with lived experience. Participants underlined that for a successful adoption and implementation, the added value, (scientific) foundation, financial basis, and organizational structure of the program should be clearly communicated. Conclusions: The findings provide directions for how, where, and by whom an adapted lifestyle program (Healthy Habits Coaching) for people with physical disabilities could be implemented in Dutch rehabilitation and community settings. This study demonstrates an example of how an evidence-based lifestyle program can be prepared for implementation in a new setting, presenting an efficient and promising strategy to enhance overall well-being among people with disabilities.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".