Rehabilitation staff perspectives on approaches to enhance personalization in program delivery for persons with neurological conditions: a qualitative study
Bibliographic record
Abstract
PURPOSE: This study aimed to explore rehabilitation staff perspectives on the delivery, personalization, and tailoring of physical activity and mobility programs for individuals with neurological conditions, focusing on patient-centered care and overcoming challenges in program implementation METHODS: Qualitative semi-structured interviews were conducted with eight rehabilitation staff members (physiotherapists, occupational therapists, fitness instructors, and rehabilitation specialists) at a rehabilitation centre in Ontario, Canada. The interviews were analyzed using the Framework Method to identify key themes related to program delivery, personalization, barriers, and potential improvements. RESULTS: Three main themes were constructed: (1) Current approaches to personalizing programs, including patient-centered goal setting and varying program delivery; (2) Barriers to personalization, such as staffing shortages, fragmented systems, and challenges with virtual programming; and (3) Potential improvements, including multi-condition programming and better integration of care partners. CONCLUSION: Rehabilitation staff face significant challenges in personalizing physical activity programs due to resource constraints, fragmented systems, and virtual delivery limitations. However, opportunities for improvement exist by addressing these barriers and enhancing program flexibility to better meet the diverse needs of patients with neurological conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".