"Nurse, I need help, too!" : Nursing interventions to support partners of patients suffering from chronic pain
Bibliographic record
Abstract
Background: Chronic pain not only affects the afflicted patient, but also their partners, who play a pivotal part in the management of chronic pain. Nurses play a key role in supporting the partner, whose needs often go forgotten. Research Questions: How do partners of those patients suffering from chronic pain experience life at home and which interventions can nurses implement to support them? Method: Two systematized literature searches were conducted in nurse relevant databases. The chosen studies were critically appraised and discussed with the Calgary Family Model. Results: The following five themes were deduced from the partners’ experiences: personal impact of chronic pain, change in personal and social relationships, support of patients, lack of personal support, and coping skills. The following interventions were established: showing belief in the partner, providing education, and offering support. Conclusion: Nurses can help create individualized interventions to best support partners of those with chronic pain by conducting an early assessment of the couple according to the processes described in the Calgary Family Model.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".