The Breast Cancer Treatment Experience for a Breast Cancer Patient Living With Intellectual or Developmental Disabilities in Ontario, Canada: A Critical Realist Case Study
Bibliographic record
Abstract
ABSTRACT Background Adult females with intellectual or developmental disabilities are more likely to die following a breast cancer diagnosis. Differences in access to cancer treatment may contribute to these survival differences. We explored how one woman with intellectual or developmental disabilities received guideline‐recommended breast cancer treatment. Methods A critical realist case study was used. The single case included a woman with intellectual or developmental disabilities previously diagnosed with breast cancer (pseudonym: Sandra), her support worker, and her surgeon. We conducted four semi‐structured interviews with (1) Sandra and her support worker, (2) her support worker, (3) her surgeon and (4) her sister. Data were analyzed using critical realist thematic analysis. Neurodiverse advisors assisted with developing easy‐read and accessible study materials. Findings Sandra had stage II breast cancer and received guideline‐recommended breast cancer treatment. Four themes were identified that influenced her breast cancer treatment: attitudes, relationships, shared decision‐making and accessible accommodations, and advocacy. Conclusions This case study provides a positive example of the type of care adult females with intellectual or developmental disabilities diagnosed with breast cancer can receive and could inform strategies for improving gaps in care including the involvement of patient navigators trained in working with this patient group.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".