Leadership of interprofessional health and social care teams: a socio-historical analysis
Bibliographic record
Abstract
AIM: The aim of this paper is to explore some of the key socio-historical issues related to the leadership of interprofessional teams. BACKGROUND: Over the past quarter of a century, there have been repeated calls for collaboration to help improve the delivery of care. Interprofessional teamwork is regarded as a key approach to delivering high-quality, safe care. EVALUATION: We draw upon historical documents to understand how modern health and social care professions emerged from 16th-century crafts guilds. We employ sociological theories to help analyse the nature of these professional developments for team leadership. KEY ISSUES: As the forerunners of professions, crafts guilds were established on the basis of protection and promotion of their members. Such traits have been emphasized during the evolution of professions, which have resulted in strains for teamwork and leadership. CONCLUSIONS: Understanding a problem through a socio-historical analysis can assist management to understand the barriers to collaboration and team leadership. IMPLICATIONS FOR NURSING MANAGEMENT: Nursing management is in a unique role to observe and broker team conflict. It is rare to examine these phenomena through a humanities/social sciences lens. This paper provides a rare perspective to foster understanding - an essential precursor to effective change management.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".