Staff attitudes and beliefs around LGBTQ issues at the Children's Hospital of Eastern Ontario
Bibliographic record
Abstract
Introduction: Pediatric specialty hospitals may be experiencing increases in the number of lesbian, gay, bisexual transgender and queer (LGBTQ) patients and parents seen. There is no published information on the attitudes, beliefs and information needs of the broad range of staff and physicians that provide care in a hospital context, although recent surveys have considered individual professional groups. We undertook such a hospital-wide survey to assess the climate and information needs of care providers. Methods: A web-based survey was opened to all staff and physicians at a tertiary care pediatric hospital in Ottawa, Canada in June 2013. Results: 315 completed surveys were analyzed. Most respondents identified as heterosexual and none identified as transgender. Approximately half were directly involved in patient care. Approximately 90% were fully comfortable around LGBTQ patients and coworkers and most felt the hospital provided fair and equitable care for all. LGBTQ-identified respondents were somewhat less positive about the climate than heterosexual respondents, and front line staff less positive than managers. Many respondents identified knowledge deficits and were receptive to additional training. Conclusions: In the context of a socially and legally liberal jurisdiction, most pediatric hospital staff are accepting of LGBTQ clientele and co-workers while some identify areas where knowledge and skill could be improved and are willing to undergo additional training in working with LGBTQ clientele. Survey results can inform policy and procedural changes as well as training initiatives.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".