What factors influence sexual and reproductive health care among adolescent and young adult cancer patients?: a novel serial focus group study
Bibliographic record
Abstract
BACKGROUND: Disparities in sexual and reproductive health care at diagnosis and during treatment for adolescent and young adult (AYA, ages 15-39) cancer patients may be linked to various factors, including those at the patient- and system-level. We conducted serial focus groups to explore how AYA cancer patients' experiences with sexual and reproductive health and its care are influenced by their identities (patient factors) and contextual enablers (healthcare factors). METHODS: We recruited individuals aged ≥ 18 years, diagnosed with cancer as an AYA, and residing in Canada. Participants were grouped into cohorts based on identity factors (e.g., gender, cancer stage) with each cohort taking in three serial focus groups mimicking support groups (e.g., sharing information, fostering community). Patient research partners contributed to topic guide development and co-facilitated focus groups. We used framework analysis guided by the PROGRESS-Plus (Place of residence, Race/ethnicity/culture/language, Occupation, Gender, Religion, Education, Social capital, Socioeconomic status, and Plus) framework on health inequity factors and Andersen's model of access to medical care. RESULTS: Altogether, 48 participants (age 33 (21-48)) were assigned into eight cohorts. Each cohort took part in three serial focus groups, resulting in a total of 24 focus groups. Among participants, there was representation based on gender (e.g., nonbinary and gender fluid, n = 4), non-heterosexual sexual orientation (n = 14), and race (n = 12). Themes indicate AYA cancer patients' experiences with lack of sexual and reproductive health information and support, and lack of consideration of experiences and beliefs when providing sexual and reproductive health care. Findings reveal eight identity (patient-level) factors (place of residence, self-advocacy (occupation, education, and plus), socioeconomic status, social capital (plus), gender and sex, age (plus), relationship status (plus), sexual orientation (plus)) and two contextual enablers (health care system-level factors) (inefficiencies and interactions) that influence access to appropriate and available sexual and reproductive health care. CONCLUSION: AYA cancer patients experiences with sexual and reproductive health care are influenced by their identity factors and contextual enablers. Treatment plans, research studies, and care programs should account for these to ensure that care is both inclusive and responsive to their unique needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".