A qualitative cancer screening study with childhood sexual abuse survivors: experiences, perspectives and compassionate care
Bibliographic record
Abstract
OBJECTIVE: The childhood sexual abuse (CSA) survivor population is substantial and survivors have been identified as part of the population who were under-screened or never-screened for breast, cervical and colon cancer. Our objective was to learn CSA survivor perspectives on, and experiences with, breast, cervical and colon cancer screening with the intention of generating recommendations to help healthcare providers improve cancer screening participation. DESIGN: A pragmatic constructivist qualitative study involving individual, semistructured, in-depth interviews was conducted in January 2014. Thematic analysis was used to describe CSA survivor perspectives on cancer screening and identify potential facilitators for screening. PARTICIPANTS: A diverse purposive sample of adult female CSA survivors was recruited. The inclusion criteria were: being a CSA survivor, being in a stable living situation, where stable meant able to meet one's financial needs independently, able to maintain supportive relationships, having participated in therapy to recover from past abuse, and living in a safe environment. 12 survivors were interviewed whose ages ranged from the early 40s to mid-70s. Descriptive saturation was reached after 10 interviews. SETTING: Interviews were conducted over the phone or Internet. CSA survivors were primarily from urban and rural Ontario, but some resided elsewhere in Canada and the USA. RESULTS: The core concept that emerged was that compassionate care at every level of the healthcare experience could improve cancer screening participation. Main themes included: desire for holistic care; unique needs of patients with dissociative identity disorder; the patient-healthcare provider relationship; appointment interactions; the cancer screening environment; and provider assumptions about patients. CONCLUSIONS: Compassionate care can be delivered by: building a relationship; practising respect; focusing attention on the patient; not rushing the appointment; keeping the environment positive and comfortable; maintaining patient dignity; sharing control whenever possible; explaining procedures; and using laughter to reduce power imbalance through shared humanity.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".