The development of an applied whole-systems research methodology in health and social service research: A Canadian and United Kingdom collaboration
Bibliographic record
Abstract
This paper provides a description of a 'whole system' research model developed in Canada and adapted for use in the United Kingdom. The model tests the assumption that service utilization across the whole system is driven by a complex mix of psychosocial factors, rather than disease characteristics, of the population and, second, that proactive, integrated and communitybased packages of care are equally or more effective and less expensive than fragmented services. The methodology uses large-scale, cross-sectional surveys of a diverse range of population groups accessing health and social services. Data were collected on measures of psychosocial resource, functional ability and service utilization. The relationship between psychosocial resources, functional ability and service utilization is analysed. This paper describes the research model, the theories informing the model and the application of the model to two diverse population groups: patients with chronic obstructive pulmonary disease and single parents on welfare. The article concludes by acknowledging that multi-agency approaches to evaluation using measures of patient need and healthcare outcomes which do not privilege the contribution of any one agency are required to test the cost-effectiveness of inter-agency working. This paper describes the early experiences of replicating this methodology in a UK context in order to test the findings for their generalizability to that context.
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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 | 0.224 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.018 | 0.023 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".