‘Not another form!’: lessons for implementing carer assessment in health and social service agencies
Bibliographic record
Abstract
This article addresses some of the issues that need to be considered in implementing carer assessment in health and social service agencies. It is based on findings from three studies involving the use of the CARE (Caregivers' Aspirations Realities and Expectations) Assessment Tool in Canada, a comprehensive psychosocial instrument. The first study, carried out between 1999 and 2001, was aimed at developing the CARE Tool, and had as one of its objectives to evaluate the feasibility of its implementation into ongoing practice. The second study, conducted between 2000 and 2003, was designed to evaluate the impact of using the CARE Tool, and also had an objective concerning implementation. A third study was undertaken in 2005-2006, in part, to gain more understanding of the barriers and outcomes of implementing carer assessment. All three studies used focus groups and individual interviews as the main data collection method. In all, this article is based on 13 focus groups and five individual interviews with home care professionals and 19 individual interviews with home care managers or supervisory staff, all having experience with carer assessment. Similar themes emerged from the thematic analyses of the data from all three projects. All studies point to the following as preconditions to successful implementation: clarification of carer status within policy and practice; making explicit agency philosophy with regard to the role and responsibilities of families in care and conceptions of carer assessment; and agency buy-in at all levels. Four themes emerged as issues for implementation: integration of the carer assessment tool with existing tools; ensuring training and ongoing supervision; work organisation and resources required for carer assessment; and logistical questions. It would appear essential that a clear rationale for moving towards carer assessment and its place in a global approach to long-term care and carers are essential to its implementation.
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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 | 0.182 | 0.205 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.020 | 0.022 |
| Scholarly communication | 0.022 | 0.035 |
| Open science | 0.014 | 0.017 |
| Research integrity | 0.018 | 0.028 |
| Insufficient payload (model declined to judge) | 0.004 | 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".