The process of decision-making in home-care case management: implications for the introduction of universal assessment and information technology
Bibliographic record
Abstract
Increasingly, jurisdictions are adopting universal assessment procedures and information technology to aid in healthcare data collection and care planning. Before their potential can be realised, a better understanding is needed of how these systems can best be used to support clinical practice. We investigated the decision-making process and information needs of home-care case managers in Ontario, Canada, prior to the widespread use of universal assessment, with a view of determining how universal assessment and information technology could best support this work. Three focus groups and two individual interviews were conducted; questioning focused on decision-making in the post-acute care of individuals recovering from a hip fracture. We found that case managers' decisional process was one of a clinician-broker, combining clinical expertise and information about local services to support patient goals within the context of limited resources. This process represented expert decision-making, and the case managers valued their ability to carry out non-standardised interviews and override system directives when they noted that data may be misleading. Clear information needs were found in four areas: services available outside of their regions, patient medical information, patient pre-morbid functional status and partner/spouse health and functional status. Implications for the use of universal assessment are discussed. Recommendations are made for further research to determine the impact of universal assessment and information technology on the process and outcome of home-care case manager decision-making.
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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 | 0.274 | 0.466 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.010 | 0.046 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".