Bibliographic record
Abstract
Objective: The main objectives of this study were to evaluate the performance of leadership roles and the nurses’ satisfaction with supervision and to examine the relationship between the performance of leadership roles and nurse satisfaction.Methods: A quantitative, descriptive, correlational and transversal study was conducted, with a sample made up of 79 nurses practicing in two public health institutions in the district of Coimbra in Portugal. Data collected included demographic characteristics, eight leadership roles and five levels of supervision satisfaction. The Munson’s Supervision Satisfaction Scale and the Quinn’s Leadership Scale were used. The instruments adaptation and validation study revealed adequate psychometric properties being considered as valid instruments for the quantitative study pursuance.Results: The results indicate an acknowledgement of all roles of leadership, having a tendency towards the roles of producer (4.73) and director (4.64) indicating leadership centered on the rational goals model, revealing a greater concern with efficiency and productivity. The role of innovator was the one least perceived (4.16). Most nurses (83.50%) were satisfied with the supervision. The relationships between all the leadership roles and nurse satisfaction were positive, with the roles of facilitator (r = .842; p < .001) and mentor (r = .871; p < .001) with the strongest correlation.Conclusions: The investigation revealed that the leader who plays all roles of leadership raises the level of supervision satisfaction of the nurses he/she leads. A poor performance of the facilitator role suggests the need for head nurses to acquire leadership skills which help them manage interpersonal conflicts and promote cohesion and teamwork within the current context of health care restructuring and decreased nurse staffing levels.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".