Conceptualising characteristics of resources withdrawal from medical services: a systematic qualitative synthesis
Bibliographic record
Abstract
BACKGROUND: Terms used to describe government-led resource withdrawal from ineffective and unsafe medical services, including 'rationing' and 'disinvestment', have tended to be used interchangeably, despite having distinct characteristics. This lack of descriptive precision for arguably distinct terms contributes to the obscurity that hinders effective communication and the achievement of evidence-based decision-making. The objectives of this study are to (1) identify the various terms used to describe resource withdrawal and (2) propose definitions for the key or foundational terms, which includes a clear description of the unique characteristics of each. METHODS: This is a systematic qualitative synthesis of characteristics and terms found through a search of the academic and grey literature. This approach involved identifying commonly used resource withdrawal terms, extracting data about resource withdrawal characteristics associated with each term and conducting a comparative analysis by categorising elements as antecedents, attributes or outcomes. RESULTS: Findings from an analysis of 106 documents demonstrated that terms used to describe resource withdrawal are inconsistently defined and applied. The characteristics associated with these terms, mainly antecedents and attributes, are used interchangeably by many authors but are differentiated by others. Our analysis resulted in the development of a framework that organises these characteristics to demonstrate the unique attributes associated with each term. To enhance precision, these terms were classified as either policy options or patient health outcomes and refined definitions for rationing and disinvestment were developed. Rationing was defined as resource withdrawal that denies, on average, patient health benefits. Disinvestment was defined as resource withdrawal that results in, on average, improved or no change in health benefits. CONCLUSION: Agreement on the definition of various resource withdrawal terms and their key characteristics is required for transparent government decision-making regarding medical service withdrawal. This systematic qualitative synthesis presents the proposed definitions of resource withdrawal terms that will promote consistency, benefit public policy dialogue and enhance the policy-making process for health systems.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.128 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.019 | 0.024 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".