Changing stress while stressing change: The role of interprofessional education in mediating stress in the introduction of a transformative technology
Bibliographic record
Abstract
The introduction of a transformative technology into practice settings can affect the functioning of interprofessional teams, placing stress on interprofessional relationships, thus slowing adoption and change. This study explored the potential of an interprofessional education (IPE) approach to mediate this stress and facilitate the adoption of a transformative technology- Image Guided Radiation Therapy (IGRT). Oncologists, physicists, and therapists in radiation medicine who attended an interprofessional IGRT Education Course were interviewed about perceived benefits and stressors to IPE and to interprofessional practice (IPP) in the IGRT context. A modified grounded theory approach was used to conduct 14 interviews, with 200 minutes of interview time recorded. In introducing IGRT, participants noted interprofessional stress in understanding and adopting new technology. IPE offered common terminology, appreciation for others' knowledge, and a holistic framework for practice. Outcomes were thought to foster collaboration, efficiency, and improved professional role definition. Time constraints and power relations were noted to be residual stressors exacerbated by IPE, but were thought to be transient. IPE can thus be of benefit in the implementation of transformative technologies such as IGRT, through mediation of interprofessional stress inherent in change. Interprofessional knowledge, collaboration, and efficiency in practice facilitate the development and adoption of a new practice model.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".