The development of a disinvestment framework to guide resource allocation decisions in health service delivery organizations
Bibliographic record
Abstract
Objective: Within publicly funded health care, there is an urgent need to reduce spending while maximizing benefits; however, processes to guide so-called disinvestment decisions are lacking. The purpose of this research is to develop a framework that will provide decision-makers with a more equitable approach to meeting budgetary constraints than current practices. Methods: Through a systematic review of the health care literature and a scoping review of the public sector and business literatures, a knowledge synthesis of disinvestment approaches was created, including analyses of current strategies and the modeling of appropriate processes. From this synthesis, a disinvestment framework has been developed. In collaboration with Chief Financial Officers from across Western Canada and an external reference group comprised of international researchers, the framework has been critiqued in keeping with current resource allocation practices. Results: Evidence from the two reviews revealed that while budgetary cutbacks are experienced across government, non-profit and the private sector, very few processes have been developed to identify and implement disinvestment options. In cases of budget re-allocation, program budgeting and marginal analysis (PBMA) was the most relevant framework described. However, PBMA fails to address stand-alone disinvestment requirements. Within the public sector and business literatures, cutback management and policy termination research offered strategies to mitigate barriers and facilitate implementation, however, details were absent. Drawing elements from the approaches identified in the reviews, and in collaboration with decision-makers and other researchers, a seven-step disinvestment framework was developed that can be incorporated into on-going priority setting practices or applied as a stand-alone activity. Conclusion: This work addresses a critical knowledge gap in how health service organizations approach disinvestment activities. The proposed framework provides detailed steps to equip health care decision makers with a clear and defined disinvestment process. Such a process will help to ensure limited funds are allocated based on evidence rather than across-the-board cuts or historical practices.
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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 | 0.113 | 0.102 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.023 | 0.013 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".