Lifestyle coaching for people living with physical disabilities: exploring perceptions of clients and professionals
Bibliographic record
Abstract
Purpose To inform the implementation of a new Dutch lifestyle coaching service (Healthy Habits Coaching) targeting clients with chronic conditions and/or physical disabilities, this study aimed to explore experiences and perceptions of clients and professionals on receiving, delivering and implementing lifestyle coaching.Materials and methods This pragmatic qualitative study was conducted in partnership with a Dutch community organisation. Semi-structured interviews were conducted with clients (n = 9) with chronic conditions and/or physical disabilities who received lifestyle coaching, and professionals (n = 10) who delivered and/or implemented lifestyle coaching. Data were analysed using reflexive thematic analysis.Results Clients and professionals elaborated on the importance of client-centred coaching and reciprocal responsibilities within a client-coach relationship. Clients emphasised the value of stepwise guidance on behaviour change and mentioned coaching evoked feelings of meaning, autonomy and improved capability. Professionals talked about increased societal attention for promoting a healthy lifestyle in people with a chronic condition or disability.Conclusion We identified key characteristics that may contribute to a successful lifestyle coaching service for people with chronic conditions and/or physical disabilities: establishing a reciprocal coach-client relationship, using a client-centred approach, and incorporating action planning techniques. These findings inform the implementation of Healthy Habits Coaching.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".