Suicide Studies and the Need for Mixed Methods Research
Bibliographic record
Abstract
The research method in suicide studies has been primarily quantitative, and suicide remains without an adequate or accepted general theory that incorporates multiple disciplines and perspectives. Dependence on quantitative research limits an understanding of the complexity of suicide. This article argues for the use of mixed methods for suicide research. Three key topics in suicide research are highlighted: risk factors for suicide, efficacy of suicide prevention, and cultural factors in suicide and suicide prevention. Mixed methods will expand knowledge of suicide by integrating theory-based variables and subjectivity as objects of inquiry. Mixed methods will allow for a broadening of research questions, more substantive understanding, and are necessary for a multidimensional and multidisciplinary suicidology.
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 | 0.652 | 0.697 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".