Medication adherence for persons with spinal cord injury and dysfunction from the perspectives of healthcare providers: A qualitative study
Bibliographic record
Abstract
Context: People with spinal cord injury and dysfunction (SCI/D) often take multiple medications (i.e. polypharmacy) to manage secondary health complications and multiple chronic conditions. Numerous healthcare providers are often involved in clinical care, increasing the risk of fragmented care, problematic polypharmacy, and conflicting health advice. These providers can play a crucial role in assisting patients with medication self-management to improve medication adherence.Design: A qualitative study involving telephone interviews, following a semi-structured guide that explored healthcare providers' conceptualization of factors impacting medication adherence for persons with SCI/D. The interviews were transcribed and analyzed descriptively and interpretively using a constant comparative process with the assistance of data display matrices. Analysis was guided by an ecological model of medication adherence.Setting and participants: Thirty-two healthcare providers from Canada, with varying clinical expertise.Intervention: Not Applicable.Outcome measures: Not Applicable.Results: Providers identified several factors that impact medication adherence for persons with SCI/D, which were grouped into micro (medication and patient-related), meso- (provider-related) and macro- (health system-related) factors. Medication-related factors included side effects, effectiveness, safety, and regimen complexity. Patient-specific factors included medication knowledge, preferences/expectations/goals, severity and type of injury, cognitive function/mental health, time since injury, and caregiver support. Provider-related factors included knowledge/confidence and trust. Health system-related factors included access to healthcare and access to medications. While providers were able to identify several factors influencing medication adherence, micro-level factors were the most frequently discussed.Conclusion: Findings from this study indicate that strategies to optimize medication adherence for persons with SCI/D should be multi-faceted.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".