Partnership, Trust and Leadership among Nursing Researchers
Bibliographic record
Abstract
Members of a nursing research cluster realized that they needed to determine whether, given their diverse philosophies, they could formulate a collective research agenda responding to an administrative recommendation. The cluster's leaders conducted an appraisal of the role and importance of trust as an element for promoting collaboration in a nursing research cluster and for building a collective social identity. The Social Exchange Theory framed the appraisal. A survey and a facilitation session about trust in research partnerships were conducted with eight female nursing researchers/faculty. Facilitation day's discussion was fully audio recorded, transcribed verbatim and the content coded using ATLAS.ti 6. Thematic analysis was employed to analyze the qualitative aspects of the recorded discussion and the survey questionnaire explanatory responses. Responses to survey closed-questions were compiled as descriptive statistics. Participants revealed that mutual support, valuing each other and working collaboratively facilitated trust in intellectual partnership. Hindering factors were an environment suppressing expression of ideas and views, lack of open dialogue and decision-making among team members and lack of a sense of belonging. This paper has the potential to contribute to the knowledge of nursing leaders who are intending to develop and sustain nursing research teams in both academic and non-academic organizations. The paper will be especially useful as they deal with issues of trust in intellectual partnership in diverse settings.
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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 | 0.054 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".