Medication adherence in patients with mental illness and recent homelessness: contributing factors and perceptions on mobile technology use
Bibliographic record
Abstract
OBJECTIVES: The primary objective was to determine medication-taking behaviours and factors influencing adherence in patients with mental illness and recent homelessness. Secondary objectives were to explore patients' perceptions on mobile technology use to support adherence. METHODS: A constructivist approach and qualitative description method was used. The sample population consisted of patients with recent homelessness and mental illness affiliated with a community-based outreach programme in Canada. Participants were purposefully selected; semi-structured interviews were conducted to elicit information on medication-taking strategies and mobile technology to support adherence. A standardized questionnaire collected demographic and medical information; the Medication Adherence Rating Scale (MARS) was used to evaluate self-reported adherence. Questionnaire data were analysed using summary descriptive statistics. Interview data were subject to qualitative content analysis. KEY FINDINGS: Fifteen participants with a mean age of 44 years were included. The mean MARS score ± standard deviation was 7.3 ± 1.5. Themes arising from the data included patient factors (i.e. insight, attitudes towards medications, coping strategies) and external factors (i.e. therapeutic alliance, family support that impacted adherence) and technology use and health. Eight participants (53%) had access to a mobile phone. There was a moderate interest in the use of mobile technology to support adherence, with cost and technology literacy identified as barriers. CONCLUSION: External supports and individual medication management strategies were important in supporting medication adherence in this patient group. Perceived need for mobile technology, in addition to existing supports for adherence, was not high. Challenges accessing and maintaining consistent mobile technology and individual preferences should be considered when developing mobile technology-based interventions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".