Bibliographic record
Abstract
Background: The process of shared decision making (SDM), a key component of interprofessional (IP) practice, provides an opportunity for the separate and shared knowledge and skills of care providers to synergistically influence the client / patient care provided. The aim of this study was to understand how different professional groups perceive IPSDM, their role as effective participants in the process and how they ensure their voices are heard. Methods: A sequential explanatory mixed methods design was used consisting of a realist review of the literature about IPSDM in intensive care, a survey of the IP team (n=96; RR-81.4%) about collaboration and satisfaction with the decision making process in NICU, semi-structured interviews with a sample of team members (n=22) working in NICU, and observation of team decision making interactions during morning rounds over a two week period. A tertiary care NICU in Canada was the study setting. Findings: The study revealed a number of key findings that are important to our increased understanding of IPSDM. Healthcare professionals’ (HCP) views differ about what constitutes IPSDM. The nature of the decision (triage, chronic condition, values sensitive) is an important influencing factor for IPSDM. Four key roles were identified as essential to the IPSDM process: professional expert, leader, synthesizer and parent. IPSDM involves collaboration, sharing, weighing and building consensus to overcome diversity. HCPs use persuasive knowledge exchange strategies to ensure their voices are heard during IPSDM. Buffering power differentials and increasing agreement about best options lead to well-informed decisions. A model was developed to illustrate the relationships among these concepts. Conclusions: Findings from this study improve understanding of how different members of the team participate in the IPSDM process, and highlight effective strategies to ensure professional voices are heard, understood and considered during deliberations.
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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 | 0.046 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".