Transforming a conservative clinical setting: <scp>ICU</scp> nurses' strategies to improve care for patients' relatives through a participatory action research
Bibliographic record
Abstract
This study focuses on change strategies generated through a dialogical-reflexive-participatory process designed to improve the care of families of critically ill patients in an intensive care unit (ICU) using a participatory action research in a tertiary hospital in the Balearic Islands (Spain). Eleven professionals (representatives) participated in 11 discussion groups and five in-depth interviews. They represented the opinions of 49 colleagues (participants). Four main change strategies were created: (i) Institutionally supported practices were confronted to make a shift from professional-centered work to a more inclusive, patient-centered approach; (ii) traditional power relations were challenged to decrease the hierarchical power differences between physicians and nurses; (iii) consensus was built about the need to move from an individual to a collective position in relation to change; and (iv) consensus was built about the need to develop a critical attitude toward the conservative nature of the unit. The strategies proposed were both transgressive and conservative; however, when compared with the initial situation, they enhanced the care offered to patients' relatives and patient safety. Transforming conservative settings requires capacity to negotiate positions and potential outcomes. However, when individual critical capacities are articulated with a new approach to micropolitics, transformative proposals can be implemented and sustained.
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 | 0.039 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".