Primary care in Northern Ontario for transgender people in the context of the <scp>COVID</scp>‐19 pandemic: A qualitative secondary analysis
Bibliographic record
Abstract
PURPOSE: To examine changes to primary care practice for transgender clients resulting from government mandated public health measures in response to COVID-19 in Northern Ontario. DESIGN: Secondary analysis of qualitative data using interview transcripts from a dataset that included 15 interviews conducted between October 2020 and April 2021. METHODS: The dataset came from a convergent mixed method study exploring the delivery of primary care services to transgender individuals in Northern Ontario. Qualitative interviews with primary care practitioners including nurse practitioners, nurses, physicians, social workers, psychotherapists, and pharmacists providing care for transgender people in Northern Ontario were included in the secondary analysis. RESULTS: Fifteen primary care practitioner providing care to transgender individuals in Northern Ontario participated in the parent study. Practitioners described their understanding of the effect of the early stages of the COVID-19 pandemic changes on their practice and the care experience for their transgender patients. Two themes were identified and described by participants: (1) a change in the delivery of care; and (2) barriers and facilitators to care. CONCLUSIONS: Practitioners' primary care experiences in the early waves of COVID suggest the integral use of telehealth in Northern Ontario transgender care. Nurses working in advance practice and nurse practitioners are essential in providing continuity of care for their transgender clients. CLINICAL RELEVANCE/SIGNIFICANCE: Identification of initial practice changes for the primary care of trans people will illuminate avenues for further research. The urban, rural, and remote practice settings in Northern Ontario provide an opportunity for increasing access for gender diverse people in these areas and for developing increased understanding of uptake of telemedicine practice. Nurses are integral to primary care for transgender patients in Northern Ontario.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".