Rebooting Kirkpatrick: Integrating Information System Theory Into the Evaluation of Web-based Continuing Professional Development Interventions for Interprofessional Education
Bibliographic record
Abstract
INTRODUCTION: Information system research has stressed the importance of theory in understanding how user perceptions can motivate the use and adoption of technology such as web-based continuing professional development programs for interprofessional education (WCPD-IPE). A systematic review was conducted to provide an information system perspective on the current state of WCPD-IPE program evaluation and how current evaluations capture essential theoretical constructs in promoting technology adoption. METHODS: Six databases were searched to identify studies evaluating WCPD-IPE. Three investigators determined eligibility of the articles. Evaluation items extracted from the studies were assessed using the Kirkpatrick-Barr framework and mapped to the Benefits Evaluation Framework. RESULTS: Thirty-seven eligible studies yielded 362 evaluation items for analysis. Most items (n = 252) were assessed as Kirkpatrick-Barr level 1 (reaction) and were mainly focused on the quality (information, service, and quality) and satisfaction dimensions of the Benefits Evaluation. System quality was the least evaluated quality dimension, accounting for 26 items across 13 studies. WCPD-IPE use was reported in 17 studies and its antecedent factors were evaluated in varying degrees of comprehensiveness. DISCUSSION: Although user reactions were commonly evaluated, greater focus on user perceptions of system quality (ie, functionality and performance), usefulness, and usability of the web-based platform is required. Surprisingly, WCPD-IPE use was reported in less than half of the studies. This is problematic as use is a prerequisite to realizing any individual, organizational, or societal benefit of WCPD-IPE. This review proposes an integrated framework which accounts for these factors and provides a theoretically grounded guide for future evaluations.
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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 | 0.217 | 0.406 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".