Identifying what matters to Canadian adults with mobility limitations regarding experiences with medications: A concept mapping study
Bibliographic record
Abstract
Background: Persons with mobility limitations generally take multiple medications to manage their condition and other health complications. There are limited assessment tools measuring the experiences with medications and their impacts on everyday life. Understanding what matters to persons about experiences with medications will inform person-centred clinical care, integrated care, ongoing monitoring, and overall quality of care. Objective: The objective of this study was to identify what matters to Canadian adults with mobility limitations regarding their experiences with medications. Methods: We conducted a concept mapping study which is a participatory, mixed methods approach. It involves six steps: preparation, brainstorming, sorting and rating, analysis, mapping and interpretation, and utilization. Participants were required to: be 18 years of age or older, live in Canada, live in the community, speak and read English or French, have a mobility limitation, and take at least one medication recommended by a prescriber in the preceding three months. During the brainstorming sessions, participants generated statements in response to the focal prompt: what matters to you about medications in your everyday life? In the sorting task, participants created piles of statements based on their conceptual similarity. In the rating task, participants rated each statement on two dimensions – importance and realistic. In the mapping session, a subset of participants created visual maps of the data. Results: Participants generated 694 statements during the brainstorming sessions, which were synthesized into a final list of 80 statements. The final map contained ten clusters that aligned with what mattered to participants about their medications in everyday life: (1) medication-related financial considerations and support; (2) pharmacy-related services and supports; (3) access to medications and medication-related supports; (4) acceptance and stigma around medication use; (5) ability and ease of taking medications; (6) shared decision-making and access to medication-related research and information; (7) medication effectiveness, side effects and risks; (8) knowledge, self-awareness and empowerment; (9) accessibility of healthcare providers; and (10) communication and relationships with healthcare providers. Medication-related financial considerations and support was the cluster rated highest on importance, but lowest on realistic. Implications and Next Steps: This research has identified key items and domains related to medication-related experiences that will inform improved healthcare delivery and outcomes for Canadian adults who take medications. In the next steps of this research, we will engage with medication prescribers, administrators, decision-makers, and patients to better understand implementation considerations around patient-reported experience and/or outcome measures, prior to the development of a measure to be used in practice. Conclusions: There is currently a lack of patient-reported experiences and/or outcome measures that apply a comprehensive assessment on the experiences with or impact of medications on everyday life. Obtaining a better understanding of and individuals’ experiences with medications and how they may impact their quality of life will help inform the co-development and implementation of an experience measure specific to medications.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".