On the same page? A qualitative study of shared mental models in an interprofessional, inter-organizational team implementing goal-oriented care
Bibliographic record
Abstract
Goal-oriented care is an approach to care delivery that uses patient-identified goals to drive care planning. Implementing goal-oriented care requires team members to cognitively shift the focus from “what is the matter” to “what matters to patients,” and align their mental models of what it means to care for patients. Yet, no empirical studies of goal-oriented care apply evidence from the cognitive sciences, such as Shared Mental Model (SMM) theory. We conducted a qualitative case study of an interprofessional team that adopted goal-oriented care in Vermont, US (n = 18). Guided by SMM theory, we distinguished between task-related and team-related mental models. We used framework analysis and qualitative content analysis to determine mental model content and similarity. The most shared content areas were operationalizing goal-oriented care, engaging in formal and informal communication, taking a “whole-person” approach, taking a team approach, and building trusting relationships with patients and with other team members. Trust was the only construct that spanned both task and team mental model categories, highlighting the importance of both intra-team trust and provider-patient trust to the implementation of goal-oriented care. Team members developed SMMs through training, regular meetings, and interactions during care delivery. This study provides insight into the cognitive mechanisms that underlie team-based goal-oriented care delivery, which can be used to inform implementation, training content, and future research.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".