Impact of Physical Activity Counselling on Children with Medical Conditions and Disabilities and Their Families
Bibliographic record
Abstract
Physical activity counselling can target cognitive-affective participation barriers, but counselling benefits for children with medical conditions/disabilities were unknown. This study investigated successes, challenges, and the impact of physical activity counselling on children and their families. One-on-one semi-structured interviews were completed with 7 patients (2 male/5 female, aged 13-17) and 4 parents who participated in 2-8 weekly counselling sessions (2015-2020). Interviews were recorded and transcribed verbatim for inductive thematic analyses. Counselling encouraged positive mindset changes (viewing physical activity more holistically, making it "more fun and manageable", helping them to "learn how to love moving and doing sports"). Participants felt strong support (feeling heard, validated, and provided with "hope… that we can still achieve things… even though it may seem like there's limitations"). Counselling was viewed positively. The intent to improve active lifestyle attitudes and confidence was reflected in positive, primarily cognitive-affective (motivation for activity, "more general skills of having a positive attitude towards physical activity and the willingness to try new things") outcomes. More sessions, additional resources to keep, and follow-up after counselling completion were recommended to support behaviour change. Future research should evaluate enhanced counselling services and comparing children who have and have not received such counselling.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".