Social Studies of Science and Technology: New ways to illuminate challenges in training for health information technologies utilisation
Bibliographic record
Abstract
CONTEXT: Electronic health records (EHRs) have transformed clinical practice. They are not simply replacements for paper records but integrated systems with the potential to improve patient safety and quality of care. Training physicians in the use of EHR is a highly complex intervention that occurs in a dynamic socio-technical health system. Training in this complex space is considered a wicked problem and would benefit from different analytic approaches to the traditional linear causal relationship analysis. Social Sciences theories see technological change in relation to complex social and institutional processes and provide a useful starting point. AIM: Our aim, therefore, is to introduce the medical education scholar to a selection of theoretical approaches from the Social Studies of Science and Technology (SSST) literatures, to inform educational efforts in training for EHR use. METHODS: We suggest a body of theories and frameworks that can expand the epistemological repertoire of medical education scholarship to respond to this wicked problem. Drawing from our work on EHR implementation, we discuss current limitations in framing training for EHRs use as a research problem in medical education. We then present a selection of alternative theories. RESULTS: Unified Theory of Acceptance and Use of Technology (UTAUT) explains the individual adoption of new technologies in the workplace and has four key constructs: performance/effort expectancy, social influence and facilitating conditions. Social Practice Theory (SPT), rather than focusing on individuals or institutions, starts with the activity or practice. The socio-technical model (STM) is a comprehensive theory that offers a multidimensional framework for studying the innovation and application of EHRs. Practical examples are provided. CONCLUSIONS: We argue that education for effective utilisation of EHRs requires moving beyond the epistemological monism often present in the field. New theoretical lenses can illuminate the complexity of research to identify the best practices for educating and training physicians.
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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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.007 | 0.093 |
| Scholarly communication | 0.018 | 0.032 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".