A qualitative study : community nurses' experience of organizational trust
Bibliographic record
Abstract
This qualitative inquiry explored nurses’ experience of organizational trust and factors that influence it within a community health context. The study aims were to provide awareness to health organizations as to how they may avoid negative impacts of decreased trust of community nurses' and increase the likelihood of positive acceptance of changing health services and a restructured organization. The British Columbia healthcare system is becoming more reliant on the community nursing sector to address increasing demands in healthcare due to several factors such as an aging population, rise in chronic diseases, and limited financial and human resources. If community nurses do not have trust in the health organization, there may be resistance to changes that have been designed to improve health services. This may result in poorer health outcomes for the population and increased burden on the acute care system. Health organizations that are not aware of factors that may influence community nurses’ organizational trust may encounter higher rates of job turnover, decreased productivity, and challenges in maintaining a viable healthcare system. A qualitative design with an interpretative description approach was used along with theoretical concepts pertaining to organizational trust. Community nurses within the interior region of British Columbia, Canada received invitations to participate via email. Data collection involved semi-structured interviews with 10 participants which were digitally recorded and transcribed professionally. Three main themes identified were 1) leadership support 2) organizational restructure 3) evolving health services and practices. These main themes yielded the following conclusions: that nurses’ perception of leadership support, particularly nurse-leader relationships can significantly influence their trust in the health organization; and that nurses’ lack of participation in decision-making during change can negatively impact their trust in the health organization. The major implications that arose from this study include: 1) implications for leadership to implement strategies to improve interpersonal relationships with nurses, collaborative change processes and a shared vision, opportunities for nurses to engage with organizational structures; 2) implications for practice to include nurses in decision-making and utilize change models; 3) implications for policy to have a nurse representative at the senior level of 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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".