Patient-Reported Outcome Measures (PROMs) to Support Adherence to Falls Prevention Clinic Recommendations: A Qualitative Study
Bibliographic record
Abstract
Daria Tai,1– 4 Eric Li,1 Teresa Liu-Ambrose,2– 4 Nick Bansback,5,6 Mohsen Sadatsafavi,7,8 Jennifer C Davis1– 3 1Social & Economic Change Laboratory, Faculty of Management, University of British Columbia – Okanagan, Kelowna, British Columbia, Canada; 2Center for Hip Health and Mobility, Vancouver Coastal Health Research Institute, University of British Columbia, Vancouver, British Columbia, Canada; 3Aging, Mobility, and Cognitive Neuroscience Lab, Department of Physical Therapy, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada; 4Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada; 5School of Population and Public Health, University of British Columbia – Vancouver, Vancouver, British Columbia, Canada; 6Centre for Health Evaluation & Outcome Sciences, University of British Columbia, Vancouver, British Columbia, Canada; 7Faculty of Pharmaceutical Sciences, University of British Columbia – Vancouver, Vancouver, British Columbia, Canada; 8Collaboration for Outcomes Research and Evaluation, University of British Columbia, Vancouver, British Columbia, CanadaCorrespondence: Jennifer C DavisFaculty of Management, The University of British Columbia – Okanagan, 1137 Alumni Avenue, Kelowna, BC V1V 1V7, CanadaTel +1 250 807 9507Email jennifer.davis@ubc.caPurpose: We examined how patient-reported outcome measures (PROMs) support patients’ adherence to fall prevention recommendations in a novel primary care setting – the Falls Prevention Clinic.Patients and Methods: Using a patient-oriented qualitative study design, we recruited patient partners to our study team to assist in developing focus group prompts. A trained facilitator conducted five semi-structured interviews with a total of 21 Falls Prevention Clinic participants. A trained facilitator prompted participants about: their views on the EuroQol 5 domain – 5 level (EQ-5D-5L) PROM, their preferences for PROM administration and feedback, the presentation of PROM questionnaire data, the use of comparative data and the EQ-5D-5L in improving adherence to recommendations, and other information they would need to improve adherence. Participants’ responses were coded according to three stages of qualitative analysis: open, axial and selective coding using an iterative and comparative approach.Results: “Opportunity” and “Development” emerged as higher-level themes for the participants’ perspectives on how the EQ-5D-5L may be helpful for their appointments. “Frequency” described how often the participants believed the EQ-5D-5L should be administered and feedback provided. “Challenges”, “Benefits”, “Patients’ Understanding”, “Relevance of Data”, and “Usefulness of Data” provided insight on how PROMs data presentation was viewed by patients. “Performance”, “Resources”, “Knowledge”, “Role in Behaviour Change” highlighted the participants’ ideas for the role of the EQ-5D-5L and additional information in supporting their adherence to falls prevention recommendations. Participants emphasized that patients would value further support information to facilitate their adherence.Conclusion: This patient-oriented qualitative study, among individuals at high risk of future falls, sheds light on the importance of timely, understandable feedback, integrated with other clinical feedback in supporting adherence.Keywords: EQ-5D-5L, patient-physician communication, administration, self-awareness, adherence, falls
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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 | 0.067 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".