Broadening the Patient Safety Agenda to Include Home Care Services
Bibliographic record
Abstract
Caring for an individual in the home is inherently complex. The physical environment, family dynamics and the cognitive abilities of the client and family members are only a few of the factors to be considered in delivering services. Although targeted initiatives have been established to reduce preventable injuries and deaths in the hospital sector, there has not been a corresponding level of research or patient safety initiatives in other healthcare delivery sectors. A coordinated and collaborative approach to generate new knowledge pertaining to safety in home care in Canada has therefore been undertaken by the Canadian Patient Safety Institute (CPSI), VON Canada, and Capital Health (Edmonton). Actions included the development of a background paper (Lang and Edwards 2006) that informed an invitational roundtable discussion, where key safety issues in home care were identified and priority actions discussed. Over 40 individuals from across Canada participated, reflecting various disciplinary and organizational affiliations in the delivery of home care services. This paper describes key findings from the background paper, outcomes from the ensuing roundtable discussions and implications for practice, research and policy.
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 | 0.043 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.024 | 0.022 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".