What Factors Influence Case Managers' Resource Allocation Decisions? A Systematic Review of the Literature
Bibliographic record
Abstract
OBJECTIVE: Case managers' decisions directly affect the amount and type of services individual clients receive, as well as overall home care program available resources. We know little about the resource allocation decision-making processes of case managers. The question guiding this review was, "What factors influence case managers' resource allocation decisions in home care?'' METHODS: The authors did a systematic literature review to answer the above question. After assessing the articles for inclusion, they assessed the quality (internal validity) of each included study. They described the characteristics of the studies and provided a synthesis of the findings of the primary studies. RESULTS: Five qualitative and 6 quantitative articles met the inclusion criteria for this review. The findings of these studies are equivocal. Despite this, the authors were able to create a preliminary taxonomy of the factors that influence case manager resource allocation decisions. Despite evidence-based decision making receiving so much attention in contemporary health care literature, the authors found a near absence of reference to research use in the context of case manager decision making. CONCLUSIONS: Currently, there are relatively few studies in the literature on the factors that influence, and how they are used in, case manager resource allocation decisions. Studies are often lacking in terms of conceptual clarity and theoretical framing. They are often not guided by theoretical frameworks and are not situated within the larger field of decision making or even within the clinical decision-making literature. These issues are impeding progress in this area.
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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 | 0.047 | 0.220 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".