Creating a patient reported outcome measure for medication-related quality of life: a concept mapping brainstorming study in Ontario, Canada
Bibliographic record
Abstract
Introduction: Persons with mobility limitations generally take multiple medications to manage their conditions; however, healthcare providers across different settings do not currently have a way to measure the impact that medication taking has on patients’ quality of life (QoL). Current tools are limited, narrowly focused on few QoL domains and none focus on persons with mobility limitations. Target Audience: The target audience for our study findings are healthcare providers working with individuals with mobility limitations across different healthcare settings. Who was involved: This study included patient and caregiver partners. They were involved in the grant application, study participant recruitment, data collection, and final data analysis. This study also involved healthcare providers who supported participant recruitment and will be knowledge users, including pharmacists, physicians, and physical therapists. Aims and Methods: The aim of this study was to identify a list of potential items for the creation of a patient reported outcome measure (PROM) from the perspectives of individuals with mobility limitations who take medications. We used a mixed methods concept mapping approach to gather perspectives from across Canada. Here, we report on the results of the qualitative brainstorming phase of the study. Participants created a list of statements reflecting what matters to them about their medications in their everyday life. The statements were de-duplicated and condensed, following which, they were thematically analyzed and mapped onto common quality of life domains. Results: Twenty-two persons with mobility limitations across Canada participated in this study. Six hundred and ninety-four statements were condensed into 80 final statements. The final statements mapped onto common quality of life domains (physical health; mental health; employment/social/leisure activities; daily activities; interactions with providers; navigating the healthcare system; autonomy and decision-making; financial health) indicating a comprehensive list of items for the creation of the PROM. In addition, we identified some mobility limitation specific concepts relating to barriers to physically accessing medications and necessary testing, and to perceived health plan or policy restrictions. Learnings for the international audience: Results from this study emphasized the importance of including patient autonomy, decision-making and financial concerns, which are often excluded from other measures related to medication taking and QoL. Additionally, we identified other topics that matter to individuals such as impact on lifestyle activities, patient self-management, ease of access to medications and necessary testing, as well as financial challenges associated with medication taking. Next Steps: The next phases of concept mapping will be conducted, which will identify statements that are important and realistic to individuals with mobility limitations and a final conceptual mapping session. Statements that are rated highly on both scales will inform the development of items for the PROM, which will be validated in future work. In developing and validating a PROM for medication-related QoL, we aim to provide clinicians with an invaluable tool to enhance and monitor clinical care and patient outcomes. We envision this PROM might be used to inform overall medication management.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".