Factors Influencing Participation in Physiotherapy Services Following a Total Shoulder Replacement Surgery: A Cross‐Sectional Survey
Bibliographic record
Abstract
Background: When designing appropriate rehabilitation programs after a shoulder replacement, it is critical to consider the multitude of factors that can influence a patient's participation. Therefore, this study aims to quantitively understand the factors that affect access and participation to physiotherapy services after a shoulder replacement in older adults. Methods: Our research team created an online of 58 questions, focusing on personal characteristics, geographic accessibility, socioeconomic status, preferences for care, social support, and cultural beliefs to understand potential barriers and facilitators to accessing services. Using a mixed-methods approach, data was analyzed through quantitative descriptive statistics and interpretive descriptive methodology. Data was stratified by gender. Data collection took place in 2020-2022. Results: A total of 51 (53% women) people participated in this survey; with the average age of 64.6 (9.4) years old. Gender heavily influenced patients' preferences on accessing care and physiotherapy services. Social factors, economic factors and personal factors emerged as potential barriers to participation in physiotherapy for women. Patient expectations differed by gender, as women prioritize return to daily activities (93%), whereas men prioritized sport/recreational activities (85%). Finally, preferences for delivery of physiotherapy differed based on gender, as men prefer in person (77%) and women prefer virtual. Conclusion: This survey was able to investigate trends that influence participation to rehabilitation after a shoulder replacement, both quantitatively and qualitatively. This study is a starting point for future research to explore how factors such as gender roles and social expectations may affect individuals' participation in rehabilitation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".