Bibliographic record
Abstract
There is abundant evidence verifying that patients benefit when nurses communicate better; therefore improving team dynamics will positively impact patient care and improve nurse engagement resulting in many positive outcomes for teams. This applied action research study assessed the research question: “What is the experience of team work and team dynamics among members of a multidisciplinary nursing team from a Licensed Practical Nurse perspective?” The experiences of licensed practical nurses (LPNs) and key external leaders (KELs) are explored and analyzed drawing from current literature in the field of teams in health, organizational culture in health, and transformative learning in health. Historical and leading communication, organizational culture, and leadership theories guide this study. During focus groups and interviews, the researcher and participants were influenced to generate new knowledge and insight on team dynamics, through appreciative inquiry. Manifest and latent content analysis identified key themes within each of the subtopic themes, generating a number of recommendations for future action. Through the identification of similar and unique perspectives between the literature and participants in this study, the action research goals of empowerment and emancipation of team members was dynamically met for research participants.\nKeywords: nursing; team dynamics; communication; collaboration; culture; leadership
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".