Translating family satisfaction data into quality improvement*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Improvement of clinical care requires measurement of key dimensions of health care quality and action based on these measurements. Families, data analysts, clinicians, and administrators all have important roles to play. OBJECTIVE: To outline an approach to the measurement and utilization of family satisfaction data so that these data can be translated into health care quality improvement initiatives. DESIGN: Using a synthesis of existing knowledge about translation of satisfaction data into improvement strategies, this approach starts with selecting and implementing a satisfaction survey that reflects the key processes, providers, and places for the delivery of critical care. The survey results can be expressed in a way that prioritizes the opportunities for improvement. A comparison of results across sites, or use of a performance-importance grid, can assist in this prioritization process. High-priority items can then be addressed by the multidisciplinary intensive care unit team using a systematic, evidence-based approach to improvement that includes implementation strategies that have been proven to effectively change clinician behavior.
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.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it