Rates and Reasons for Concealing Suicidal Ideation From Clinicians: A Cross‐National Examination
Bibliographic record
Abstract
INTRODUCTION: Nondisclosure of suicidal ideation (SI) presents a challenge to assessing suicide risk, as assessments often rely on self-reported SI. Cross-national comparisons of rates of, and reasons for, SI nondisclosure remain understudied. The present study examined whether rates of SI nondisclosure from clinicians differed, and if there were differences in rates of reasons for SI nondisclosure, across 12 countries. METHOD: A subset of adult participants (n = 6770) who reported a lifetime history of SI from 12 countries responded to the question: "Have you ever denied or concealed thinking about suicide from a doctor or clinician?" Those who selected "Yes" provided reason(s) for SI nondisclosure. Responses were coded according to inductively and deductively derived reasons for SI nondisclosure. RESULTS: Rates of nondisclosure among participants who reported lifetime SI ranged from 7.2% (Taiwan) to 48.2% (United States). Among those participants, rates of reasons for SI nondisclosure ranged from 2.0% (participant was unsure/did not know why they concealed SI) to 35.6% (negative internal experiences). CONCLUSIONS: Findings provide insight into the frequency of and reasons for SI nondisclosure from clinicians at a country-specific level. Results could serve as a rationale for clinicians to incorporate indirect suicide risk factors into risk assessments.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".