Routine and adaptive expert strategies for resolving ICT mediated communication problems in the team setting
Bibliographic record
Abstract
CONTEXT: The use of information and communication technologies (ICTs) for supporting interprofessional communication is becoming increasingly common in health care. However, little research has explored how ICTs affect interprofessional communication, or how novices are trained to be effective interprofessional ICT users. This study explores the interprofessional communication strategies of nurses and doctors (trainees and experts) when their communications were mediated by a specific ICT: an electronic patient record (EPR). METHODS: A total of 72 doctors and nurses participated in this 8-month study on a paediatric in-patient ward. Eighty hours of non-participant observations and 20 semi-structured interviews were conducted. All data were rendered anonymous prior to analysis. Using a constructivist grounded theory approach, one researcher read and analysed all data recursively. As emergent themes were identified, exemplary portions of the data were discussed with three additional researchers to resolve discrepancies and confirm the coding structure. Expertise literatures informed the final analyses. RESULTS: Three interprofessional communication strategies were identified: (i) all participants routinely formulated 'workarounds' to circumvent problematic EPR-mediated communications; (ii) workarounds were classifiable as instances of Abandoning, Forcing or Submitting to the EPR, and (iii) novices learned workaround strategies through an informal curriculum, but they did not learn to manage the interprofessional effects of these workarounds. CONCLUSIONS: Trainees relied on workarounds as simplified routines, demonstrating routine expertise. Staff members, demonstrating adaptive expertise, used workarounds as part of a broader network of people and communication tools. Explicit training regarding this network and the ways in which workarounds conceal this network may help trainees develop adaptive expertise.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".