Understanding the interaction between suicidality and transition-related care: An interpretive phenomenological analysis of perspectives from trans individuals and mental health and medical care providers
Bibliographic record
Abstract
The current study explored trans individuals’ and medical and mental health care providers’ understanding of the interactions between suicidality and transition-related care. The topic was queried using qualitative methodology, specifically interpretive phenomenological analysis (IPA). Participants included 7 trans individuals and 11 mental health and medical care providers who work with trans individuals in or around three Canadian cities. Data analysis produced 4 superordinate themes: Contributing factors to suicidal ideation and behaviour; Factors that decrease suicidal ideation and behaviour; Clinical work with trans individuals; and Recommendations from participants regarding suicidality and transition-related care. Each superordinate theme had 2 to 7 subordinate themes, and several subordinate themes contained subthemes. Results both support and expand on exiting literature on the topics of transition-related care and suicidality among trans individuals. For example, results show that access to timely transition-related care is a factor that decreases suicidal ideation and behaviour, as well as show that the quality of care is important, as empowering transition-related care that supports trans individuals’ autonomy and self-determination was shown to be particularly beneficial towards decreasing suicidality. Lastly, implications of the results towards future research and clinical work are discussed. For example, providers who approve or deliver transition-related care are encouraged to work from a client-centered, informed consent, transparent, affirmative and culturally-competent care model, in which they work to provide care in a timely, empowering, and personalized way to trans clients
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 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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".