The perceptions of women in Northern Ontario about their reproductive healthcare
Bibliographic record
Abstract
With a goal to improve service provision, this study examined the experiences of women in Northern Ontario with respect to their uptake of reproductive healthcare services. As part of my mixed methods study, women in Northern Ontario completed a survey, offered in English and French, about their reproductive healthcare experiences, and a portion of participants were also interviewed. A multimodal recruitment strategy and maximum variation sampling was applied, with a goal of collecting the experiences of a diverse group of women. Analysis was based on 173 completed surveys and 19 semi-structured qualitative interviews. The interview data is grouped under five conceptual constructs, with “gender” as an overarching construct, found to be a factor in the remaining four: the characteristics of good care; the relationship with the provider; the care environment, and administrative practices. Additionally, respondents felt that midwives excelled at providing quality reproductive healthcare and would favour increased access to midwives for reproductive health care throughout their lives. For the survey data, I used descriptive statistics and multivariate linear regression models to determine whether residency, language, education, income, overall health, access to care, having a family physician, and preferring female providers (independent variables) were associated with the perceived quality of reproductive healthcare services and choosing a midwife. Most survey participants rated their reproductive healthcare experiences as fair to good overall but indicate room for improvement in the relationships with their providers and administrative support. Women residing rurally, without a family physician, and lower socioeconomic status are statistically more likely to prefer a midwife for reproductive healthcare. The survey results indicate a preference for female healthcare providers by the majority of interviewees. Changes driven by strong health policy may be required to engage women in recommended reproductive healthcare more fully. Although northern geography will always present challenges that may not be subject to amelioration, other factors preventing women from accessing care are more amenable to change, and as argued here, this is a right of women and an obligation on the part of governments to provide equitable access for all Canadians to healthcare services.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".