Adverse Events in Community Care: Developing a Research Agenda
Bibliographic record
Abstract
Little is known about the extent to which adverse events compromise the quality of community care. This article describes the results of a consensus workshop in which 31 healthcare professionals were asked to identify and rank common adverse events and important research questions relating to community care. Workshop participants were decision-makers and healthcare providers with areas of expertise that included community and home care; acute and primary care; patient safety; medical errors; and health services policy, administration and research. Results include prioritized lists of adverse events, research questions and contributing factors associated with adverse events. Further study should be aimed at defining and implementing research priorities and developing standardized definitions of common adverse events associated with community care.
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 | 0.239 | 0.191 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.017 | 0.009 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.028 | 0.050 |
| Open science | 0.008 | 0.026 |
| Research integrity | 0.031 | 0.023 |
| Insufficient payload (model declined to judge) | 0.006 | 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".