Nurse‐led post‐thoracic surgery pain management programme: its outcomes in a Nigerian Hospital
Bibliographic record
Abstract
AIM: The overarching aim of this study was to investigate the effects of a nurse-led pain management programme on pain intensity, side effects of treatment, shoulder range of motion and length of stay after thoracic surgery. BACKGROUND: Post-thoracic surgical pain is a major source of stress and distress for patients. It has profound effects ranging from increased risks in developing chronic post-thoracic surgery pain to an increased length of stay after surgery. The post-thoracic surgical pain management in the Nigerian context is based on the traditional approach that is dependent on the attending medical and nursing staff. METHODS: The study was a quasi-experimental design (two-group post-test only). The study was conducted in a Nigerian hospital. Forty-two patients were recruited and consecutively assigned into either the usual pain management group or the intervention group after they had met the inclusion criteria. Data were collected utilizing the following: (1) the modified McGill Pain Questionnaire; (2) a Numeric Rating Scale; (3) the documentation form for thoracic surgery pain management outcomes and (4) a goniometer. RESULTS: The findings indicated that pain intensity, nausea and drowsiness were significantly reduced among the patients in the experimental group than the control group, while the duration of stay after surgery and the shoulder range of motion were not different between the groups. CONCLUSION: This study's results suggest that the intervention in question for patients undergoing thoracic surgery had a positive effect on reducing pain intensity, nausea and drowsiness but not the shoulder range of motion and length of stay after surgery. IMPLICATIONS FOR NURSING POLICY: Nursing policymakers may need to give a serious consideration to the revision of policies related to the nursing education curriculum as well as the in-service training curriculum regarding pain management by nurses especially after surgery. Likewise, future research on other populations employing an improved methodology as well as utilizing up-to-date evidence by nurses across different hospitals may be necessary.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 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".