Integrating Technology Adoption Models Into Implementation Science Methodologies: A Mixed-Methods Preimplementation Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Sustainable implementation of patient-oriented technologies in health care settings is challenging. Preimplementation studies guided by the Consolidated Framework for Implementation Research (CFIR) can provide opportunities to address barriers and leverage facilitators that can maximize the likelihood of successful implementation. When looking to implement patient-oriented technologies, preimplementation studies may also benefit from guidance from a conceptual framework specific to technology adoption such as the Unified Theory of Acceptance and Use of Technology. This study was, therefore, aimed at identifying determinants for the successful implementation of a patient-oriented technology (i.e., automated pain behavior monitoring [APBM] system) within a health care setting (i.e., long-term care [LTC] facility). RESEARCH DESIGN AND METHODS: Using a mixed-methods study design, 164 LTC nurses completed a set of questionnaires and 68 LTC staff participated in individual interviews involving their perceptions of an APBM system in LTC environments. Quantitative data were analyzed using a series of mediation analyses and narrative responses were examined using directed content analysis. RESULTS: Performance expectancy and effort expectancy partially and fully mediated the influence of implementation, readiness for organizational change, and technology readiness constructs on behavioral intentions to use the APBM system in LTC environments. Findings from the qualitative portion of this study provide guidance for the development of an intervention that is grounded in the CFIR. DISCUSSION AND IMPLICATIONS: Based on our results, we offer recommendations for the implementation of patient-oriented technologies in health care settings.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".