ISQUA16-2674THE ACCREDITATION CANADA PROGRAM: FACILITATING INTEGRATION ACROSS A HEALTH SYSTEM AND THE CONTINUUM OF CARE
Bibliographic record
Abstract
This presentation showcases how accreditation can be used as a catalyst to facilitate the integration of quality improvement efforts across an entire province-wide health authority and across the continuum of care. In the fall of 2014, legislation created two health authorities in the Canadian province of Nova Scotia. Effective April 2015, nine health authorities were amalgamated under one governing body. While the second health authority, the Izaak Walton Killam Health Centre, retained its governance structure, the formation of an integrated health authority created the need for a new approach to accreditation in the province for the newly-created entity, the Nova Scotia Health Authority (NSHA). In order to ensure a consolidated approach to quality and safety and to obtain a common baseline, the overall NSHA 2017 on-site survey has been designed to inform three key question lines: ... Interim bridging on-site surveys in 2016 for the former health authorities are being used to ensure that standards for high risk areas such as Infection Prevention and Control and Medication Management are maintained. Self-assessments and organizational questionnaires on patient safety culture and worklife are also being deployed in 2016 to provide a comprehensive gap analysis and to inform an action plan for integrated quality 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 | 0.031 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.004 |
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".