Evaluation of CancerChatCanada: A Program of Online Support for Canadians Affected by Cancer
Bibliographic record
Abstract
BACKGROUND: Professional-led cancer support groups can improve quality of life and address unmet needs, but most Canadians affected by cancer do not have access to or do not make use of cancer support groups. A collaborative interdisciplinary team developed, operated, and evaluated Internet-based, professional-led, live-chat support groups (osgs) for cancer patients, caregivers, and survivors across Canada. OBJECTIVE: Our study aimed to report participant and participation characteristics in the pan-Canadian initiative known as CancerChatCanada, and to understand participant perspectives about the quality of communication and professional facilitation, overall satisfaction, and psychosocial benefits and outcomes. METHODS: Participants in osgs provided informed consent. Participant and participation characteristics were gathered from program data collection tools and are described using frequencies, means, and chi-squares. Patient, survivor, and caregiver perspectives were derived from 102 telephone interviews conducted after osg completion and subjected to a directed qualitative content analysis. RESULTS: The 55 professional-led osgs enrolled 351 participants from 9 provinces. More than half the participants came from rural or semirural areas, and more than 84% had no received previous cancer support. The attendance rate was 75%, the dropout rate was 26%, and 80% of participants were satisfied or very satisfied. The convenience and privacy of osgs were benefits. Meaningful communication about important and difficult topics, kinship and bonding with others, and improved mood and self-care were perceived outcomes. CONCLUSIONS: Our results demonstrate that this collaborative initiative was successful in increasing reach and access, and that pan-Canadian, professional-led osgs provide psychosocial benefit to underserved and burdened cancer patients, survivors, and family caregivers.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".