Silos and Social Identity: The Social Identity Approach as a Framework for Understanding and Overcoming Divisions in Health Care
Bibliographic record
Abstract
CONTEXT: One of health care's foremost challenges is the achievement of integration and collaboration among the groups providing care. Yet this fundamentally group-related issue is typically discussed in terms of interpersonal relations or operational issues, not group processes. METHODS: We conducted a systematic search for literature offering a group-based analysis and examined it through the lens of the social identity approach (SIA). Founded in the insight that group memberships form an important part of the self-concept, the SIA encompasses five dimensions: social identity, social structure, identity content, strength of identification, and context. FINDINGS: Our search yielded 348 reports, 114 of which cited social identity. However, SIA-citing reports varied in both compatibility with the SIA's metatheoretical paradigm and applied relevance to health care; conversely, some non-SIA-citers offered SIA-congruent analyses. We analyzed the various combinations and interpretations of the five SIA dimensions, identifying ten major conceptual currents. Examining these in the light of the SIA yielded a cohesive, multifaceted picture of (inter)group relations in health care. CONCLUSIONS: The SIA offers a coherent framework for integrating a diverse, far-flung literature on health care groups. Further research should take advantage of the full depth and complexity of the approach, remain sensitive to the unique features of the health care context, and devote particular attention to identity mobilization and context change as key drivers of system transformation. Our article concludes with a set of "guiding questions" to help health care leaders recognize the group dimension of organizational problems, identify mechanisms for change, and move forward by working with and through social identities, not against them.
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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 | 0.028 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.024 | 0.013 |
| Science and technology studies | 0.008 | 0.074 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".