Understanding adherence to assistive devices among older adults: a conceptual review
Bibliographic record
Abstract
Purpose: The aim of this study was to identify and examine how existing literature has conceptualized adherence to assistive devices (ADs) among older adults.Methods: English articles were searched in MEDLINE, PubMed, and CINAHL (January 1990 to October 2017) for the key words “acceptance”, “adherence”, “assistive devices”, “compliance”, “concept,” and relevant synonyms. Bibliographies of selected articles were also examined. Articles were analyzed if the following conditions were met conjointly: (1) attempted to define or conceptualize adherence to some degree; (2) were concerned with any AD for older adults; (3) were concerned with adults aged 65 years or older.Results: Sixteen of the 484 articles were included. Adherence to ADs among older adults seemed to be conceptualized under three core themes: psychological, contextual, and functional factors; each with their own unique considerations related to adherence that are analyzed in this study.Conclusion: This review identified a large gap in knowledge about adherence to ADs. Adherence is multi-factorial and highly specific to the individual’s circumstances and their relationship with their health care practitioner. Further empirical research should focus on how the three core themes of adherence interact with and influence each other.Implications for rehabilitationHealth care professionals who assess for, and recommend ADs should foster a shared decision-making relationship with their clientsThis review identifies some of the key themes that practitioners should consider when developing and implementing AD regimens with older adultsConceptualizing AD adherence among older adults will help improve monitoring of and quality of care for AD users
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".