“If We Got a Win–Win, You Can Sell It to Everybody”: A Qualitative Study Employing Normalization Process Theory to Identify Critical Factors for eHealth Implementation and Scale-up in Primary Care
Bibliographic record
Abstract
OBJECTIVES: Translation of eHealth research findings and successful implementation into clinical care is limited. We used a multitiered approach (individual, organizational, societal) to assess the implementation potential of MyDiabetesPlan within Ontario's primary care system and applied the normalization process theory (NPT) to explicate our findings. METHODS: Data were collected from 15 individuals through interviews with primary care administrative end-users and a focus group discussion with Ministry of Health decision-makers, then qualitatively analyzed using thematic analysis for emergent themes. RESULTS: We identified 3 themes corresponding to our multitiered approach: 1) stakeholder buy-in was critical to engagement and was impacted by perceptions/capacities; 2) clinical integration of MyDiabetesPlan depended on alignment with clinic philosophy of care, pre-existing technologies and workflow; and 3) political climate and trends were important considerations for eHealth implementation. Application of NPT to findings revealed that interplay between buy-in and perceptions/capacities of clinical practice stakeholders was critical to engaging them for eHealth implementation. In contrast, evaluation of costs and outcomes was critical to inform operational-management stakeholders' perceptions. Findings at the organizational and societal levels best aligned with the factors influencing operationalization of MyDiabetesPlan. Overall, our findings show that the synergistic operationalization of MyDiabetesPlan into practice was a prerequisite to implementation at all health-care levels. CONCLUSIONS: Application of NPT revealed context- and stakeholder-specific interactions that should be synergistically leveraged to promote MyDiabetesPlan normalization into routine clinical practice. Our findings provide further insight into how researchers can comprehensively assess eHealth implementation potential within Ontario and can be extrapolated to similar single-payer health-care jurisdictions.
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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 | 0.031 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".