Adoption of an innovation: The story behind preventive services in a community health centre
Bibliographic record
Abstract
Aim. The aim of this organizational case study was to explore how the adoption of an eHealth innovation was influenced by practitioner training and organizational improvement in the delivery of smoking prevention and cessation to youth in a community health centre in Ottawa. Objectives. To examine whether the adoption of an eHealth intervention would be facilitated by a high level of coherence between: (a) the organization's level of functioning, (b) capacity to implement organizational change and (c) organizational supports for behaviour change and eHealth training. Also, to investigate whether various characteristics related to the organization, the practitioners and the innovation would influence the adoption decision process. Conclusion. Training may still be an effective intervention strategy for behaviour change when it is coupled with organizational leadership. Adoption of an innovation represents a complex interplay of characteristics of the innovation, participants and the organization. Given that collective efficacy is a potent determinant of practitioner behaviour, further research on the influence of organizational capacity on practitioner behaviour is warranted. Subjects and Setting. The study was conducted in a community health centre in Ottawa from 2004-2005 involving 12 health care practitioners and 26 adolescents (12-18 years). Results. Training did not influence practitioners' adoption of the eHealth innovation and quality improvement program as expected. Low compatibility was important as a determinant in the non-adoption-decision. The organization had deficiencies in the five critical elements required for organizational change. Practitioner self-efficacy was not a key determinant in behaviour change, while collective efficacy was a key determinant in practitioner adoption behaviour. Methods. This organizational case study had three phases: (1) Baseline: performance evaluation and needs analyses, (2) Intervention: practitioner training on using eHealth resources, and organizational improvement and (3) Maintenance. Key informant interviews and case-based interviews (Chart Stimulated Recall) were conducted. An ecological view and grounded theory were used to organize and analyse eHealth innovation determinants at the macro, meso and micro level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.017 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| 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".