Qualitative Evidence in Trauma Research: The Case of the Journal of Traumatic Stress
Bibliographic record
Abstract
The debate over the relative merits of qualitative and quantitative methods is particularly salient in the helping professions, where day-to-day clinical practice is potentially informed by research. Despite the growth in the use of qualitative methods and increasing recognition of their usefulness and relevance, particularly since the development of standards for evaluating their quality, the ratio of qualitative to quantitative articles published in journals within the helping professions tends to be small. In the context of previous studies that have shown that editorial interest in qualitative research considerably outweighs qualitative submissions and publications, we examine articles in the Journal of Traumatic Stress (JTS) to determine whether this pattern extends to the field of traumatic stress studies. Findings indicate that despite consistent interest in multidisciplinary approaches—including qualitative designs—expressed by the journal’s editors, the publication of qualitative articles in the JTS has declined since 1988. Potential explanations and effects of this discrepancy are offered.
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 | 0.179 | 0.281 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.034 | 0.071 |
| Scholarly communication | 0.028 | 0.022 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.013 | 0.010 |
| 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".