Examining social media peer support and improving psychosocial outcomes for young women with breast cancer.
Bibliographic record
Abstract
162 Background: Young women with breast cancer (YWBC) have unique survivorship needs due to life stage at point of diagnosis. Peer support sought by YWBC through social media channels appears to be rising. We aimed to understand the unmet needs of YWBC in order to develop a tailored peer support program to improve young women’s breast cancer experience and ultimately reduce psychosocial morbidity long-term. Methods: Using qualitative inquiry, we conducted semi-structured interviews with YWBC survivors and clinicians using purposive sampling. Inclusion criteria were women aged 40 years or younger at diagnosis, stage 0-IV disease. Survivors were minimum one year post-diagnosis and with active treatment complete. Interviews were recorded and transcribed verbatim and data was analyzed using Thorne’s Interpretive Description. Themes were reviewed with study team throughout data analysis. Results: Thirty-six participants were interviewed from ten centers across seven Canadian provinces; mean age 36 years. Participant reported demographics:18% ‘visible minority’, 9% ‘born outside Canada’, 7% ‘Indigenous’ and 54% of patients’ household income at or below Canadian average. At point of diagnosis 69% married, 44% had children and 9% pregnant or postpartum. Themes from YWBC interviewed focused on coping needs: feeling alone, misunderstood by professionals and misplaced among peers. Participants described all-age peer support groups risked triggering anxieties, lacked convenience and were comprised of women at later life stages with differing needs. YWBC reported lack of young age breast cancer-specific peer support. YWBC frequently found support through social media de novo, by following young-age breast cancer survivor pages, blogs and forums as well as virtual support groups. YWBC also report benefit from identifying similar life and cancer stage survivors globally and forming individual relations virtually, through direct messaging. Additionally, benefits described from age-specific social media support included unique shared experience and understanding, hope from positive outcomes of similar life stage diagnoses, and increased confidence and healthcare navigation for YWBC. Women unanimously requested one on one peer support program development - a survivor mentorship scheme specifically for YWBC that would provide the convenience of online support without the obligations or emotionally overwhelming nature of structured support groups. Conclusions: We have identified unique support needs from this young cohort of women that are not currently being met within standard Canadian healthcare pathways. We aim to develop a novel one on one peer support program for YWBC, to optimize psychosocial support and improve young women’s empowerment and autonomy in managing the effects of cancer long-term.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".