Bibliographic record
Abstract
How can health care providers (HCPs) working with 2SLGBTQ+ patients enact a whole person care approach during the SARS-CoV-2 pandemic and its aftermath, and in such desperate times, is it even reasonable to expect them to? In this presentation, a nurse/nursing educator and a health care researcher/frequent patient discuss their observations and experiences of whole person care during the SARS-CoV-2 pandemic. The conversation highlights that in the immediate chaos early on, and in the face of exhaustion, trauma, and burnout as the pandemic progressed, attending to the whole personhood of patients was/is paramount for HCPs and for the people they treat. The presenters reflect on the amplified significance of a whole person approach for 2SLGBTQ+ people who may have had negative health care experiences in the past, and may fear that they will not receive equitable care in the chaotic context of a pandemic. A whole person care approach is perhaps most necessary when it is also most difficult. In a period of such profound distress, a deeper sense of connectedness to patients may help HCPs manage feelings of helplessness they are likely to encounter, and surely helps the people they treat. The goal of this presentation is to begin a discussion about the ways that whole person approaches benefit 2SLGBTQ+ patients as well as their HCPs, with the hope that it will spark ideas for attendees to develop in their own practices.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.039 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.010 | 0.021 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".