Immigrant women cancer survivors’ perceptions of healthcare services in Canada: A phenomenological study
Bibliographic record
Abstract
Middle Eastern immigrant women (MEIW) living in Canada have significantly increased. However, this group of women is under-represented in health research, and there is a gap in knowledge about their experiences when they access healthcare services for cancer care in Canada. This qualitative approach was conducted to uncover the meaning of the lived experiences of MEIW with healthcare services in Canada during their cancer survivorship (CS). Data were collected through unstructured interviews and one written description from three MEIW. Data were analyzed using a descriptive phenomenological approach developed by Giorgi. Four themes emerged to represent the essence (or meaning) of the participants’ lived experiences. Their healthcare was accompanied with delays and unmet needs. Yet, they found it helpful when they were provided with knowledge and information. The ability to communicate in English was equal to empowerment for each of them, while they faced cultural stigmatization of mental health issues. Thus, healthcare professionals need to identify immigrant women’s unmet support needs and psychosocial responses during their cancer survivorship. Language-specific and culturally competent cancer-care intervention programs must be developed within the Canadian healthcare system.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".