Implementation of a participatory management model: analysis from a political perspective
Bibliographic record
Abstract
AIM: To analyse experiences of managers and nursing staff in the implementation of participatory management, specifically processes of decision-making, communication and power in a Canadian hospital. BACKGROUND: Implementing a Participatory Management Model involves change because it is focused on the needs of patients and encourages decentralisation of power and shared decisions. METHODS: The study design is qualitative using observational sessions and content analysis for data analysis. We used Bolman and Deal's four-frame theoretical framework to interpret our findings. RESULTS: Participatory management led to advances in care, because it allowed for more dialogue and shared decision making. However, the biggest challenge has been that all major changes are still being decided centrally by the provincial executive board. CONCLUSIONS: Managers and directors are facing difficulties related to this change process, such as the resistance to change by some employees and limited input to decision-making affecting their areas of responsibility; however, they and their teams are working to utilise the values and principles underlying participatory management in their daily work practices. IMPLICATIONS FOR NURSING MANAGEMENT: Innovative management models encourage accountability, increased motivation and satisfaction of nursing staff, and improve the quality of care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".