“Anticipate the need”: a narrative analysis of service providers’ experiences working with sexual and gender minority youth in British Columbia, Canada, during the COVID-19 pandemic
Bibliographic record
Abstract
This study explores service providers’ accounts of working with sexual and gender minority (SGM) youth and the improvised and non-institutionalized adaptations to their delivery of care in response to the COVID-19 pandemic. We present a narrative analysis of data from qualitative, in-depth semi-structured interviews conducted between July 2020 and August 2021 with 16 service providers who deliver programs and services for SGM youth in British Columbia (BC), Canada. Drawing on a central narrative of uncertainty in driving improvised adaptations to service provision amid the pandemic, we identified three sub-narratives: (i) uncertainty as characteristic of liminality; (ii) uncertainty as conducive to cooperation and collaboration; and (iii) uncertainty as enabling ‘blue-sky thinking’ and innovation. In each sub-narrative, we document service providers’ accounts of how they navigated both uncertainty in the absence of direction from their organizations and constraint by COVID-19 public health mandates and guidelines. Amid pandemic-driven interruptions in structured guidance, these improvised practices of care became key in shaping the delivery of care to SGM youth in BC. These accounts offer insights into how uncertainty can be harnessed as a potent source of improvement of services for SGM youth now and throughout future phases of the COVID-19 pandemic.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.030 | 0.022 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".