Predisposing, reinforcing, and enabling factors of trans-positive clinical behavior change: A summary of the literature
Bibliographic record
Abstract
Transgender, transsexual, and other gender minority (trans) populations experience numerous health disparities in comparison to cisgender (non-trans) groups. Many trans and gender nonconforming people report interactions with health care providers who lack knowledge about the client's specific health care needs and in some cases discrimination from health care providers, which leads to health care avoidance behaviors and poor health outcomes. Trans-positive health care interventions are necessary in order to improve health care access and outcomes for this marginalized group. In this paper we (a) synthesize literature in the areas of trans-positive care and clinical behavior change according to predisposing, reinforcing, and enabling factors, factors to be addressed as a part of the Precede-Proceed model, a model to develop and evaluate behavior change interventions, and (b) discuss future directions for research and program development with the goal of improving access to competent and quality health care for trans populations.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".