The effectiveness of sensory stimulation therapy to strengthen the resilience of operating room nurses
Bibliographic record
Abstract
BACKGROUND: Operating room (OR) nurses need to be resilient in order to cope with extreme demands in their workplace. This research focused on the effectiveness of sensory stimulation therapy (SST) to strengthen the resilience of nurses in the OR of a private hospital in the North West Province. PURPOSE: The purpose was to determine the effectiveness of SST as an intervention to strengthen the resilience of OR nurses. DESIGN: A quasi-experimental design was used. METHOD: The population consisted of OR nurses and ICU nurses at private hospitals in the North West Province. All-inclusive sampling was used. Forty-one OR nurses formed the intervention group. A pilot group (8 subjects, OR nurses), as well as a comparison group (23 subjects, ICU nurses), was also sampled. An intervention, namely SST, was implemented with the intervention group. The resilience of the intervention group, pilot group and comparison group was measured before and after the implementation of the SST by means of Wagnild and Young's resilience questionnaire. The intervention group also completed a self-report questionnaire on their needs and suggestions for SST and wrote short narratives on their experience of SST. Data were analysed using descriptive and inferential statistics, and by thematic coding. RESULTS: Results indicated a significant statistical increase in the intervention group's resilience levels. Results from the narratives confirmed that the intervention group's resilience may have been strengthened through SST. CONCLUSION: SST has potential to strengthen the resilience of OR nurses.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".