Group Interventions for Patients with Cancer and HIV Disease: Part IV. Clinical and Policy Recommendations
Bibliographic record
Abstract
Group interventions have assumed a growing role in primary prevention and supportive care for cancer and HIV disease. Earlier sections of this Special Report examined empirical findings for these interventions and provided recommendations for future research. The current section offers brief recommendations for service providers, policymakers, and stakeholders. Group services now occupy an increasingly prominent place in primary prevention programs and medical settings. In previous sections of this Special Report (Sherman, Leszcz et al., 2004; Sherman, Mosier et al., 2004a, 2004b) we examined the efficacy of different group interventions at different phases of cancer or HIV disease, considered characteristics of the intervention and the participants that might influence outcomes, and discussed mechanisms of action. Methodological challenges and priorities for future research were highlighted. In this, the final section, we offer brief recommendations for service providers, policymakers, and other stakeholders. We consider some of the barriers that constrain use of empirically-based group interventions and note how these programs might be implemented more widely and effectively.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".