Supportive and collaborative interdependence: Distinguishing residents’ contributions within health care teams
Bibliographic record
Abstract
INTRODUCTION: Individual assessments disregard team contributions, while team assessments disregard an individual's contributions. Interdependence has been put forth as a conceptual bridge between our educational traditions of assessing individual performance and our imminent challenge of assessing team-based performance without losing sight of the individual. The purpose of this study was to develop a more refined conceptualisation of interdependence to inform the creation of measures that can assess the interdependence of residents within health care teams. METHODS: Following a constructivist grounded theory approach, we conducted 49 semi-structured interviews with various members of health care teams (e.g. physicians, nurses, pharmacists, social workers and patients) across two different clinical specialties-Emergency Medicine and Paediatrics-at two separate sites. Data collection and analysis occurred iteratively. Constant comparative inductive analysis was used, and coding consisted of three stages: initial, focused and theoretical. RESULTS: We asked participants to reflect upon interdependence and describe how it exists in their clinical setting. All participants acknowledged the existence of interdependence, but they did not view it as part of a linear spectrum where interdependence becomes independence. Our analysis refined the conceptualisation of interdependence to include two types: supportive and collaborative. Supportive interdependence occurs within health care teams when one member demonstrates insufficient expertise to perform within their scope of practice. Collaborative interdependence, on the other hand, was not triggered by lack of experience/expertise within an individual's scope of practice, but rather recognition that patient care requires contributions from other team members. CONCLUSION: In order to assess a team's collective performance without losing sight of the individual, we need to capture interdependent performances and characterise the nature of such interdependence. Moving away from a linear trajectory where independence is seen as the end goal can also help support efforts to measure an individual's competence as an interdependent member of a health care team.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".