Obstacles to Evidence-Based Procurement, Implementation, and Evaluation of Health and Welfare Technologies in Swedish Municipalities: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Health and welfare technologies (HWTs) are interventions that aim at maintaining or promoting health, well-being, quality of life, and increasing efficiency in the service delivery system of welfare, social, and health care services, while improving the working conditions of the staff. Health and social care must be evidence-based according to national policy, but there are indications that evidence for HWT effectiveness is lacking in related Swedish municipal work processes. OBJECTIVE: This study aimed to investigate whether the evidence is used when Swedish municipalities procure, implement, and evaluate HWT, and if so, the kinds of evidence and the manner of their use. The study also aimed to identify if municipalities currently receive adequate support in using evidence for HWT, and if not, what support is desired. METHODS: An explanatory sequential mixed methods design was used with quantitative surveys and subsequent semistructured interviews with officials in 5 nationally designated "model" municipalities regarding HWT implementation and use. RESULTS: In the past 12 months, 4 of 5 municipalities had required some form of evidence during procurement processes, but the frequency of this varied and often consisted of references from other municipalities instead of other objective sources. Formulating requirements or requests for evidence during procurement was viewed as difficult, and gathered evidence was often only assessed by procurement administration personnel. In total, 2 of 5 municipalities used an established process for the implementation of HWT, and 3 of 5 had a plan for structured follow-up, but the use and dissemination of evidence within these were varying and often weakly integrated. Standardized processes for follow-up and evaluation across municipalities did not exist, and those processes used by individual municipalities were described as inadequate and difficult to follow. Most municipalities desired support for using evidence when procuring, establishing evaluation frameworks for, and following up effectiveness of HWT, while all municipalities suggested tools or methods for this kind of support. CONCLUSIONS: Structured use of evidence in procurement, implementation, and evaluation of HWT is inconsistent among municipalities, and internal and external dissemination of evidence for effectiveness is rare. This may establish a legacy of ineffective HWT in municipal settings. The results suggest that existing national agency guidance is not sufficient to meet current needs. New, more effective types of support to increase the use of evidence in critical phases of municipal procurement and implementation of HWT are recommended.
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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 | 0.309 | 0.336 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".