Knowledge gaps in existing research exploring sexual fluidity and mental health among young adults
Bibliographic record
Abstract
While there is a large body of evidence indicating that sexual minority youth experience inequitably high rates of mental health problems (eg, depression, suicidality), we know little about how temporal changes in sexual attractions, identities and behaviour may impact mental health (and other) outcomes. In this essay, we review existing research regarding sexual fluidity and mental health among young adults in order to identify critical knowledge gaps with respect to an epidemiological understanding of the relationship between these factors. We describe three gaps that in turn inform a larger public health research agenda on this topic. First, there are a number of methodological challenges given that fluidity can occur over short or long periods of time and across multiple dimensions of sexual orientation (eg, attractions, identities and behaviour) with various patterns (eg, directionality of change). Tailored measures that accurately and inclusively reflect diversities of sexual fluidity trajectories are needed. Second, causal relationships between sexual fluidity and mental health remain uncertain and unquantified. Third, little is known about how features of context (eg, gender norms and political climate) influence youth experiences with sexual fluidity and mental health. Finally, we propose a set of recommendations to address these knowledge gaps to improve the quality of epidemiological research involving young people.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".