The NASSS-CAT Tools for Understanding, Guiding, Monitoring, and Researching Technology Implementation Projects in Health and Social Care: Protocol for an Evaluation Study in Real-World Settings
Bibliographic record
Abstract
BACKGROUND: Projects to implement health care and social care innovations involving technologies are typically ambitious and complex. Many projects fail. Greenhalgh et al's nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework was developed to analyze the varied outcomes of such projects. OBJECTIVE: We sought to extend the NASSS framework to produce practical tools for understanding, guiding, monitoring, and researching technology projects in health care or social care settings. METHODS: Building on NASSS and a complexity assessment tool (CAT), the NASSS-CAT tools were developed (in various formats) in seven co-design workshops involving 50 stakeholders (industry executives, technical designers, policymakers, managers, clinicians, and patients). Using action research, they were and are being tested prospectively on a sample of case studies selected for variety in conditions, technologies, settings, scope and scale, policy context, and project goals. RESULTS: The co-design process resulted in four tools, available as free downloads. NASSS-CAT SHORT is a taster to introduce the instrument and gauge interest. NASSS-CAT LONG is intended to support reflection, due diligence, and preliminary planning. It maps complexity through stakeholder discussion across six domains, using free-text open questions (designed to generate a rich narrative and surface uncertainties and interdependencies) and a closed-question checklist; this version includes an action planning section. NASSS-CAT PROJECT is a 35-item instrument for monitoring how subjective complexity in a technology implementation project changes over time. NASSS-CAT INTERVIEW is a set of prompts for conducting semistructured research or evaluation interviews. Preliminary data from empirical case studies suggest that the NASSS-CAT tools can potentially identify, but cannot always help reconcile, contradictions and conflicts that block projects' progress. CONCLUSIONS: The NASSS-CAT tools are a useful addition to existing implementation tools and frameworks. Further support of the implementation projects is ongoing. We are currently producing digital versions of the tools, and plan (subject to further funding) to establish an online community of practice for people interested in using and improving the tools, and hold workshops for building cross-project collaborations. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/16861.
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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: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Protocol 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 | 0.204 | 0.175 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.044 | 0.013 |
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".