Bibliographic record
Abstract
In the Fall of 2006 four organizations representing six hospitals and health regions from across Canada participated in a study funded by the Canadian Patient Safety Institute (CPSI) to look at the measurement of patient safety culture in healthcare organizations. A survey of patient safety culture in Healthcare Organizations was sent to all direct care providers, clinical care managers, direct and non-direct care support staff and non-direct care managers in all sectors including pre-hospital care, acute care, long term care, community care, and mental health in these six organizations. Staff in administrative departments were excluded as the survey instrument is not relevant to this group. The survey included items in five areas: (1) organizational leadership for safety; (2) unit leadership for safety; (3) perceived state of safety; (4) shame and repercussions of reporting; and (5) safety learning behaviours. Of 22,624 surveys that were sent out, 6243 were returned for a response rate of 28%. Response rates ranged from 18 % to 34 % across the six organizations. Data gathered through this initiative can be used to drive change initiatives in several ways: (1) looking at high and low performance on individual survey items, (2) focusing on questions that
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 | 0.021 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.129 | 0.064 |
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".