Psychosocial predictors of mobility assistive devices non-adherence among older adults
Bibliographic record
Abstract
Background Mobility assistive devices (MADs) provide support to older adults to improve their quality of life; however, research shows that as many as 75% of older adults are non-adherent to prescribed MADs. This study investigated the psychosocial factors that predict non-adherence to MADs among older adults.Methods A sample of Canadian older adult MADs users who resided in a long-term care facility was included. The data was collected using the Psychosocial Impact of Assistive Devices Scale (PIADS), and the Medical Outcomes Study Social Support Survey (mMOS-SS). Data analysis was performed using SPSS 28. Descriptive statistics were used to describe the sample and the study variables. Pearson correlation coefficients were used to evaluate the association between the study variables. Variables that were associated with non-adherence in a univariate analysis were subsequently entered into a multiple regression analysis. Results: The sample comprised 48 residents (26 females and 22 males), with a mean age of 86.8. In the univariate analysis, scores from the three PIADS subscales, namely, Competence, Adaptability, and Self-esteem, and the Social Support scale were significantly correlated with non-adherence (p < 0.05). In the multiple regression analyses, only Self-esteem significantly predicted non-adherence (p < 0.05), and this model explained between 43.5 and 54.3% of the variance in non-adherence.Conclusion This study revealed that the Self-esteem construct, which includes several concepts related to psychological well-being, was the only significant predictor of non-adherence among the studied sample of older adults. The clinical implications of the findings are subsequently discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".