Correctional staff knowledge, attitudes and behaviors toward incarcerated trans people: A scoping review of an emerging literature
Bibliographic record
Abstract
Background: Trans people are incarcerated at disproportionately high rates relative to cisgender people and are at increased risk of negative experiences while incarcerated, including poor mental health, violence, sexual abuse, dismissal of self-identity, including poor access to healthcare.Aims: This scoping review sought to identify what is known about the knowledge, attitudes, and behaviors of correctional staff toward incarcerated trans people within the adult and juvenile justice systems.Method: This scoping review was conducted in accordance with the five-stage iterative process developed by Arksey and O’Malley (Citation2005), utilizing the PRISMA guidelines and checklist for scoping reviews and included an appraisal of included papers. A range of databases and grey literature was included. Literature was assessed against predetermined inclusion and exclusion criteria, with included studies written in English, online full text availability, and reported data relevant to the research question.Results: Seven studies were included with four using qualitative methodologies, one quantitative, and two studies employing a mixed methods approach. These studies provided insights into the systemic lack of knowledge and experience of correctional staff working with trans people, including staff reporting trans issues are not a carceral concern, and carceral settings not offering trans-affirming training to their staff. Within a reform-based approach these findings could be interpreted as passive ignorance and oversights stressing the importance of organizational policies and leadership needing to set standards for promoting the health and wellbeing of incarcerated trans persons.Conclusions: From a transformational lens, findings from this study highlight the urgent need to address the underlying structural, systemic, and organizational factors that impact upon the knowledge, attitudes, and behaviors staff have and hold in correctional, and other health and community settings to meaningfully and sustainably improve health, wellbeing, and gender-affirming treatment and care for trans communities, including make possible alternative methods of accountability for those who do harms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".