Self-help groups: Oncology nurses’ perspectives
Bibliographic record
Abstract
During the past decade in North America, the number of self-help groups for cancer patients has grown dramatically. Nurses' knowledge and attitudes about self-help groups could influence their practice behaviours and the information they provide to cancer patients. However, little is known about oncology nurses' views regarding self-help groups. This study used a cross-sectional survey to gather information about knowledge, attitudes, and practice behaviours of Canadian oncology nurses regarding self-help groups. A total of 676 nurses completed the survey (response rate of 61.3%). The respondents had spent, on average, 21.6 years in nursing and 11.6 years in oncology nursing. Results indicated that a large majority of nurses knew about available self-help groups. Approximately one-fifth of the nurses are speaking frequently about self-help groups with patients (20.7%) and are initiating the conversation on a frequent basis (22.0%). Overall, oncology nurses rated self-help groups as helpful with regards to sharing common experiences (79.5%), sharing information (75.6%), bonding (74.0%), and feeling understood (72.0%). The most frequently identified concern regarding the groups was about misinformation being shared (37.9%), negative effects of associating with the very ill (22.1%), and promoting unconventional therapies (21.2%). Implications from the study suggest that oncology nurses would benefit from learning more about the nature of self-help groups and being able to talk with patients about the self-help experience.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".