An Ethnography of Interprofessional Interactions in Discharge in a Canadian Acute Care Setting
Bibliographic record
Abstract
Governments and hospital administrators are concerned with patient discharge from hospital, assessing efficiency and quality through length of stay, readmission rates, and discharge summaries. There is less attention being awarded to how health care workers practice and interact in relation to discharge within the context of professional roles, boundaries, and organizational structures. This thesis reports on an ethnographic study using theories of medical dominance and negotiated order to explore the nature of, and interplay between, meso-structural factors that shape interprofessional interactions in discharge in a general internal medicine unit and micro-level interprofessional interactions. Data collection, conducted from January 2012 to May 2013, involved observations, interviews, and documentary analysis. Approximately 65 hours of observations were undertaken, 23 semi-structured interviews were conducted with health care workers, and government and hospital discharge documents were collected. Data were analyzed using a directed content approach. The findings indicate that the division of healthcare labour and managerialism in the form of discharge policy are affecting health care workers' roles and opportunities for interprofessional interactions. While patterns of medical dominance exist, these occur amongst changing professional roles and interprofessional boundaries, due to the impact of managerialism on the range of healthcare workers. Consequently, there were varying opportunities for interprofessional negotiations to occur around discharge. The impact of meso-structural factors and the nature of interprofessional interactions were analyzed in relation to decision-making about patient readiness for discharge and preparation for discharge, and in the context of interprofessional rounds. A further factor affecting interprofessional interactions in relation to discharge examined in this study is the existence of a medical clinical teaching unit. An understanding of the impact of meso-structural factors on interprofessional interactions and the nature of the micro-level negotiations that occur is important for the development of interprofessional education and practice interventions.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.026 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".