Identifying existing approaches used to evaluate the sustainability of evidence-based interventions in healthcare: an integrative review
Bibliographic record
Abstract
BACKGROUND: There is limited evidence to evaluate the sustainability of evidence-based interventions (EBIs) for healthcare improvement. Through an integrative review, we aimed to identify approaches to evaluate the sustainability of evidence-based interventions (EBIs) and sustainability outcomes. METHODS: Following Whittemore and Knafl's methodological process: (1) problem identification; (2) literature search; (3) data evaluation; (4) data analysis; and (5) presentation, a comprehensive search strategy was applied across five databases. Included studies were not restricted by research design; and had to evaluate the sustainability of an EBI in a healthcare context. We assessed the methodological quality of studies using the Mixed Methods Appraisal Tool. RESULTS: Of 18,783 articles retrieved, 64 fit the inclusion criteria. Qualitative designs were most commonly used for evaluation (48%), with individual interviews as the predominant data collection method. Timing of data collection varied widely with post-intervention data collection most frequent (89%). Of the 64 studies, 44% used a framework, 26% used a model, 11% used a tool, 5% used an instrument, and 14% used theory as their primary approach to evaluate sustainability. Most studies (77%) did not measure sustainability outcomes, rather these studies focused on sustainability determinants. DISCUSSION: It is unclear which approach/approaches are most effective for evaluating sustainability and what measures and outcomes are most commonly used. There is a disconnect between evaluating the factors that may shape sustainability and the outcomes approaches employed to measure sustainability. Our review offers methodological recommendations for sustainability evaluation research and highlights the importance in understanding mechanisms of sustainability to advance the field.
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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 | Metaresearch Domain: Evaluation · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | 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 | 0.115 | 0.285 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.075 | 0.042 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".