Interprofessional collaboration and family member involvement in intensive care units: emerging themes from a multi-sited ethnography
Bibliographic record
Abstract
This article presents emerging findings from the first year of a two-year study, which employed ethnographic methods to explore the culture of interprofessional collaboration (IPC) and family member involvement in eight North American intensive care units (ICUs). The study utilized a comparative ethnographic approach - gathering observation, interview and documentary data relating to the behaviors and attitudes of healthcare providers and family members across several sites. In total, 504 hours of ICU-based observational data were gathered over a 12-month period in four ICUs based in two US cities. In addition, 56 semi-structured interviews were undertaken with a range of ICU staff (e.g. nurses, doctors and pharmacists) and family members. Documentary data (e.g. clinical guidelines and unit policies) were also collected to help develop an insight into how the different sites engaged organizationally with IPC and family member involvement. Directed content analysis enabled the identification and categorization of major themes within the data. An interprofessional conceptual framework was utilized to help frame the coding for the analysis. The preliminary findings presented in this paper illuminate a number of issues related to the nature of IPC and family member involvement within an ICU context. These findings are discussed in relation to the wider interprofessional and health services literature.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".