Exploring interprofessional collaboration in the intensive care unit
Bibliographic record
Abstract
Background: Critical care units require an interprofessional management approach to optimise patients' health. Clinical education and training delivered in remote healthcare settings are vital for fostering interprofessional collaboration (IPC) among health science students for future team functioning. Objectives: Our study explored the IPC among clinicians in the intensive care unit (ICU) setting at two South African decentralised clinical training facilities to understand the existing collaborative practices that students are exposed to during their clinical training. Method: A qualitative study design, utilising semi-structured interviews, was used to gather information on the experiences of 40 purposively selected participants working in the ICU settings at the two clinical sites. Data collected from the interviews were transcribed verbatim and thematically analysed. Results: Four major themes were identified from the data, namely, scope-of-practice dispute, teamwork disruption, organisational obstacles and future aspirations. Conclusion: Participants believed that a lack of professional regard by medical doctors and an inadequate understanding of the role of other professionals impeded appropriate referral practice and collaborative team functioning. Under-exposure to interprofessional education (IPE) at an undergraduate level and the pervasive medical hierarchy were perceived as a primary attributable cause of this phenomenon. Moreover, the coronavirus disease 2019 (COVID-19) pandemic and persistent staff shortages purportedly obstructed potential opportunities to collaborate in multidisciplinary meetings. Participants believed that improving undergraduate IPE and compulsory multidisciplinary meetings to promote communication would improve team functioning in these clinical settings. Clinical Implications: Undergraduate IPE is a feasible approach to improve collaborative care in ICUs to achieve better patient outcomes.
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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 | 0.022 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| 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".