A Road Map to Building Ethics Capacity in the Home and Community Care and Support Services Sector
Bibliographic record
Abstract
There are unique ethical issues that arise in home and community care because of its locus and range of service. However, the academic literature on ethical issues in the sector of home and community care and support remains minimal. Opportunities for education, collaboration and exchange among professionals and care providers are also severely limited. Although the proposed solution of developing ethics capacity in the home care setting is over 20 years old, only modest progress had been made until recently. This article introduces the Community Ethics Network (CEN), a replicable network of home and community care agencies in the Greater Toronto Area. Its achievements can be attributed to a commitment to work toward a common approach to ethical decision-making and to a focus on education, case reviews and policy development. CEN has produced numerous positive outcomes; key among these is the development and delivery of standardized training on ethics to more than 2,000 front-line staff of diverse backgrounds/professions and representing over 40 different organizations.
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 | 0.077 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.021 | 0.048 |
| Scholarly communication | 0.031 | 0.029 |
| Open science | 0.005 | 0.043 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".