Why Don’t Cancer Survivors Attend Cancer Support Groups in Toronto
Bibliographic record
Abstract
Background: Cancer is the leading cause of death for both men and women in Canada. Professionally or nonprofessionally led support groups have been recognized as a significant source of psychosocial support for cancer survivors. However, the participation rate was low and reasons for leaving a support group were not explored fully. Purpose: To explore the reasons why Chinese cancer survivors left or did not attend a cancer support group in Toronto. Methods: In-depth individual qualitative interviews were conducted. Five Chinese cancer survivors participated in in-depth interviews. Colaizzi’s phenomenological method was used to analyze the interview data. Results: Four themes were extracted from the in-depth interviews: “not fit in”, “not satisfied with the information provided”, “tried to be a normal person”, and “lack reliable transportation and convenient scheduling”. Conclusion: Cancer support groups can improve cancer survivors’ physical and psychosocial outcomes. The services can also help cancer survivors to obtain health related information and connect with professionals and peers. In recognizing the reasons why cancer survivors left support groups, health-care providers need to evaluate and be aware of the needs and difficulties for cancer survivors to attend support groups. They should match cancer survivors with appropriate groups. More language-friendly groups need to be launched, so cancer patients can easily find a suitable one from their neighborhood.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".