Of Novices and Experts: Settling Expertise Differences Following a Hospital Merger
Bibliographic record
Abstract
While much is known about expertise integration across different occupations, researchers have paid less attention to how expertise integration occurs when distinct groups from the same occupation need to work together. This study examines how communities of experts in a single occupation settle their practice differences and converge (or not) on common practices following a hospital merger. We conducted a two-year ethnographic study of how two neonatal intensive care units belonging to Canada's largest hospital network synchronized their care practices in a merger. Contrary to expectation, we find that novices, a group that usually has the lowest power and status in an organization, may play a pivotal role in challenging differences and enhancing an organization’s ability to improve expert practices in the context of a merger. Successfully standardizing practice may entail the enactment of rules that reflect best practices and accepted scientific evidence in the occupation's explicit body of knowledge; however, while scientific knowledge provides a foundation for settling differences, experts often push back against rules because working to them diverges from the established and tacit ways by which experts perform their work (i.e., through intuition and judgement based on experiential knowledge). Thus, managers may need a complex repertoire of interventions to facilitate practice convergence, but in the process, ironically, experts may have to regress to working according to rules, much as novices do in their quest to become an expert.
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 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".