Evaluation of virtual training delivery for health information systems implementation in Canada: A qualitative study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: As health information systems (HIS) become a critical part of patient care, it is crucial to build an effective education strategy that facilitates the adoption and sustained use of these systems. The COVID-19 pandemic (2019-2023) has contributed to the rapid shift in virtual education and training for healthcare staff. OBJECTIVE: We sought to evaluate the efficacy and long-term sustainability of virtual training for using a HIS by examining opportunities and challenges. METHOD: An exploratory, multimethods study was conducted with staff who had taken part in a virtual HIS training program as part of the clinical transformation journey at a large academic health science center in Canada. The study was guided by the Accelerating the Learning Cycle framework. Data were collected through pre- and post-training surveys, as well as semi-structured interviews. An iterative, inductive, constant comparative analysis approach, outlined by Braun and Clarke, was taken to thematically analyse the data. RESULTS: Of the 33 participants in this study, 13 were educational champions, and 20 were end-users. The pre- and post-training surveys yielded a total of 1479 responses in both groups. Three prominent themes emerged from this study: (1) fostering dynamic facilitation techniques to cultivate an inclusive culture and adapt to diverse learning needs; (2) integrating practical learning activities that contribute to knowledge retention; and (3) ensuring training resources are accessible and consistent for an optimal training experience. CONCLUSION: As HIS continue to be part of the transformation of the healthcare ecosystem, education is vital in preparing healthcare providers to perform their clinical tasks and effectively use these technologies. Findings from this study can be used to inform the development of virtual training that is inclusive and addresses the needs of care providers.
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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 | 0.015 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it