Barriers and facilitators to using a clinical decision support tool for the management of osteoarthritis pain in patients undergoing hemodialysis: a qualitative study
Bibliographic record
Abstract
Abstract Background While osteoarthritis is a significant issue within the hemodialysis population and contributes to reduced quality of life, pain related to osteoarthritis is poorly managed by healthcare professionals (HCPs) in hemodialysis settings due to the absence of clinical guidance applicable to this population. The purpose of this study was to explore the perceptions of HCPs on the barriers and facilitators to using a clinical decision support tool for osteoarthritis pain management in the hemodialysis setting. Methods A qualitative descriptive study was conducted. Purposeful and snowball sampling techniques were used to recruit hemodialysis clinicians from academic and community settings across multiple Canadian provinces. One-to-one interviews were conducted with clinicians using a semi-structured, open ended interview guide informed by the Theoretical Domains Framework, a behavior change framework. A general inductive approach was applied to identify the main themes of barriers and facilitators. Results A total of 11 interviews were completed with 3 nephrologists, 2 nurse practitioners and 6 pharmacists. Findings revealed 6 main barriers and facilitators related to the use of the clinical decision support tool. Alignment of the tool with practice roles emerged as a key barrier and facilitator. Other barriers included challenges related to the dialysis environment, varying levels of clinician comfort with pain medications, and limited applicability of the tool due to patient factors. An important facilitator was the intrinsic motivation among clinicians to use the tool. Conclusions Most participants across the included hemodialysis settings expressed satisfaction with the clinical decision support tool and acknowledged its overall potential for improving osteoarthritis pain management among patients on hemodialysis. Future implementation of the tool may be limited by existing roles and practices at different institutions. Increased collaboration among hemodialysis and primary care teams may promote uptake of the tool.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".