Development of a Functional and Psychosocial Evaluation Toolkit Using Mixed Methodology in a Community-Based Physical Activity Program for Childhood Cancer Survivors
Bibliographic record
Abstract
Abstract Purpose The evidence demonstrating the benefits of exercise and PA in patients and survivors of childhood cancer has been translated into a handful of community-based programs, such as the Pediatric cancer patients and survivors Engaging in Exercise for Recovery Program (PEER). In order to support the translation of research to practice, the next step in knowledge translation is to evaluate program effectiveness. An evaluation must consider the goals of the PEER program, feedback from key stakeholders and logistics of this program. Thus, the purpose of this study was to develop an evaluation toolkit with an algorithm for implementation for the PEER program. Methods Semi-structured interviews were conducted with three different groups (stakeholders in pediatric oncology, PEER parents and PEER participants). The interviews were transcribed and coded by two independent reviewers. Results Key themes extracted from the interviews were split into physical and psychosocial themes. The most commonly reported psychosocial themes were QOL, fatigue/energy levels, fun and confidence levels; and physical themes included motor skills, physical literacy and physical activity levels. Tools were compiled into the evaluation based on key themes identified as well as logistics of PEER. An algorithm was developed to tailor the evaluation to participants based on age, cognitive ability and mobility. Conclusion To date, this is the first evaluation toolkit and algorithm developed for a specific community-based PA program, the PEER program. The next step in knowledge-translation will be to implement the evaluation to assess feasibility, and share the evaluation for adoption within other developing programs.
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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 | 0.128 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".