Establishing best practices in cancer online support groups: protocol for a realist review
Bibliographic record
Abstract
INTRODUCTION: Considerable observational evidence suggests that cancer online support groups reduce feelings of isolation, depression and anxiety, enhance coping and self-management, and lead to better informed patients. Other studies indicate that cancer online support groups can increase distress. Yet no studies theorise the complex, context-dependent mechanisms by which cancer online support groups generate their-sometimes contrasting-outcomes. METHODS AND ANALYSIS: Guided by an integrated knowledge translation approach and the strategy for patient-oriented research, we will conduct a realist review of cancer online support groups in partnership with stakeholders. We will follow Pawson's five steps and existing quality standards to develop a program theory that explains how cancer online support groups work, for whom and in what circumstances. The specific research questions will be: what positive and negative outcomes have been reported on cancer online support groups? What are the mechanisms that are associated with these outcomes, in which contexts and for whom? Through a rigorous review of relevant scientific and grey literature, as well as ongoing dialogue with stakeholders, a program theory will be developed to explain who benefits from cancer online support groups and who does not, what benefits they derive (or do not), and the factors that affect these outcomes. ETHICS AND DISSEMINATION: The use of secondary data for this review precludes the need for ethical approval. Dissemination will be informed by the knowledge-to-action framework and will consist of tailored knowledge products that are conceived of collaboratively with stakeholders. These will include peer-reviewed publications on how cancer online support groups can be optimised and best practice recommendations to maximise the benefits experienced by people with cancer. These traditional scientific outputs, along with their respective evidence summaries, will be amplified through strategic social media events hosted and promoted by knowledge users. PROSPERO REGISTRATION NUMBER: CRD42021250046.
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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 | 0.130 | 0.146 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.121 | 0.030 |
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".