From uniprofessionality to interprofessionality: dual vs dueling identities in healthcare
Bibliographic record
Abstract
Healthcare systems are at times still viewed as siloed performances of single professions, wherein some groups hold hierarchical positions based on their expertise and prestige, rather than a collective functioning of interprofessional teams. Current policies, procedures, and regulations in healthcare education and practice seem to contribute to this context in which the various health and social care professions are set in opposition to one another. The historical, and still prominent, uniprofessional education and socialization practices position health and social care professions to view each as rivals and threats toward achieving their profession/al advancement and growth. The transformation from uniprofessionality to interprofessionality in healthcare requires the application of interprofessional socialization not just at the individual level, but also at the professional and system levels. In this process of interprofessional socialization, we need to embrace the uniqueness of each profession while cultivating an interprofessional collaboration culture in the system (dual identity). In so doing, we can facilitate a shifting mind-set, culture, operations, and policies in healthcare to recognize and foster the contribution and accountability of each profession toward achieving the quadruple aim of better care, better health, better value, and better work experience.
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 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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.046 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.022 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".