Barriers of EQ5D-5L implementation into routine clinical practice: A multisite evaluation.
Bibliographic record
Abstract
11 Background: As numbers of cancer survivors increase, health-related quality of life and healthcare service utilization warrant closer attention, which requires routine valuations such as health utility scores (e.g. EQ5D-5L). EQ5D-5L implementation into routine clinical practice requires systematic evaluation to assess project scalability with goals of eventual roll-out across all 15 Ontario cancer centres in all disease sites. Methods: We used the Canadian Institutes of Health Research’s Knowledge-to-Action (KTA) framework as a guide to assess implementation of EQ5D-5L in two outpatient cancer populations; St. Michael’s Hospital’s general breast cancer clinic (GBR) and Princess Margaret Cancer Centre’s multidisciplinary brain metastases clinic (MBM), chosen to represent two very different organizational structures and patient populations. KTA steps from landscape assessment and engagement of stakeholders through to pilot implementation using paper surveys are reported. Results: After assessing 270 patients (GBR = 137; MBM = 117) across 57 days, implementation issues at the two sites were noted. GBR clinic’s larger and more general patient base was associated with a lower average socioeconomic status than MBM clinic, which targets a specialized patient population. More barriers to implementation at GBR were systemic and organizational in nature, whereas barriers at MBM were associated with patient management, where patients’ functional disabilities or neglect to return completed questionnaires hindered data collection. For both sites, successful EQ5D-5L implementation was contingent on senior management support and engagement of multiple stakeholders throughout the implementation process, leading to site-specific suggestions. Conclusions: Differing implementation strategies at both sites is reflective of target sites’ distinctive systemic and organizational characteristics and findings can be used to inform the translation of EQ5D-5L to other sites. We present recommendations to aid scalability and implementation efforts, including future transition to electronic routine assessments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.054 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".