Beyond Individualism and Independence: Exploring Collectivism and Interdependence as Paradigms of Healthcare and Health Professions Education
Bibliographic record
Abstract
Purpose: Healthcare is delivered by teams, yet individualism continues to be foregrounded in training and assessments. In contrast to individualism is collectivism, a sociocultural phenomenological lens that considers the degree to which people are integrated into groups. Along with collectivism is interdependence, which characterizes how two or more people or things are interconnected with each other. The purpose of this review was to examine the current body of work on collectivism and interdependence to examine their potential within healthcare and health professions education (HPE). Method: This narrative review included English-language publications from peer-reviewed journals between 1990 and 2024, and excluded non-English articles, perspective pieces, commentaries, and studies that were unrelated to healthcare or HPE. The initial search in January 2024 included MEDLINE, Embase, PsycINFO and ERIC databases and was updated in November 2024 with forward and backward reference mining. Results: In total, 3,415 articles were identified, of which 63 were included in the review. We used reflexive thematic analysis to consolidate the literature. Our analysis developed four interconnected themes: 1) reshaping the culture of healthcare and the learning climate of HPE, 2) accounting for individual contributions to the team, 3) the many facets of leadership, and 4) belongingness as an essential step towards collective well-being. Together, these themes expose tensions between individualistic educational structures and the team-based realities of clinical work, and highlight pathways toward relational, trust-based learning environments. Conclusion: Healthcare and HPE can be effectively examined through the paradigms of collectivism and interdependence, which transcend and challenge the current models of individualism and independence. Collectivism and interdependence offer conceptual and practical tools to reorient HPE toward relational accountability, shared leadership, and compassionate belonging. By embracing these guiding principles, healthcare workers and educators can better prepare for the collaborative, team-based nature of healthcare, and create protective and transformative learning environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".