The challenges of using physical restraint in intensive care units in Iran: A qualitative study
Bibliographic record
Abstract
BACKGROUND: Physical restraint is widely used in intensive care units to ensure patient safety, manage agitated patients, and prevent the removal of medical equipment connected to them. However, physical restraint use is a major healthcare challenge worldwide. AIM: This study aimed to explore nurses' experiences of the challenges of physical restraint use in intensive care units. METHODS: This qualitative study was conducted in 2018-2019. Twenty critical care nurses were purposively recruited from the intensive care units of four hospitals in Tehran, Iran. Data were collected via in-depth semi-structured interviews, concurrently analyzed via Graneheim and Lundman's conventional content analysis approach, and managed via MAXQDA software (v. 10.0). FINDINGS: Three main themes were identified (i) organizational barriers to effective physical restraint use (lack of quality educations for nurses about physical restraint use, lack of standard guidelines for physical restraint use, lack of standard physical restraint equipment), (ii) ignoring patients' wholeness (their health and rights), and (iii) distress over physical restraint use (emotional and mental distress, moral conflict, and inability to find an appropriate alternative for physical restraint). CONCLUSION: Critical care nurses face different organizational, ethical, and emotional challenges in using physical restraint. Healthcare managers and authorities can reduce these challenges by developing standard evidence-based guidelines, equipping hospital wards with standard equipment, implementing in-service educational programs, supervising nurses' practice, and empowering them for finding and using alternatives to physical restraint. Nurses can also reduce these challenges through careful patient assessment, using appropriate alternatives to physical restraint, and consulting with their expert colleagues.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".