Making Quality Improvement Data Meaningful for Long-Term Care Administrators
Bibliographic record
Abstract
Abstract Tailoring feedback data to engage end-user stakeholders when sharing organizational context data is a central component of quality improvement and integrated knowledge translation. For over a decade, our research team has collected survey data (using the validated Alberta Context Tool) on modifiable aspects of organizational context from long-term care (LTC) staff (e.g., nurses, unregulated providers) across a representative cohort of 94 LTC facilities in Western Canada. We have fed back data at the facility and care unit level with the goal of making research findings more useful for decision-making and aiding improvement efforts. We have used a binary method (more favourable / less favourable organizational context) to report multidimensional data. While useful to our stakeholders (e.g., administrators) we are continually seeking ways to increase the detail in our reporting, while maintaining usability for stakeholders. We have now developed a more detailed method – the context rank summary, which displays rankings of care units within and across LTC facilities. In this study, we used a qualitative descriptive design to explore perspectives of administrators and managers (leaders) from LTC facilities on the two different methods for reporting survey data. We conducted a total of three focus groups with 16 leaders in the Maritimes and Ontario, Canada. Transcripts were analysed using content analysis. Leaders preferred a feedback report that combines a binary method with the greater detail of the context rank summary. Providing organizational context data that is more meaningful, relevant and actionable could offer an additional path to identifying areas for improvement.
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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 | 0.178 | 0.371 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.007 |
| 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".