Nightmare frequency as a function of age, gender, and September 11, 2001: Findings from an Internet questionnaire.
Bibliographic record
Abstract
Retrospective estimates of nightmare frequency for a sample of 23,990 re-spondents to an Internet questionnaire (female: N 19,367, mean age 24.9 10.14 years; male: N 4,623; mean age 25.5 10.81) were evaluated as a function of age, gender, and pre- versus post-September 11, 2001. Female respondents reported more frequent monthly nightmares (4.44 6.71) than did male respondents (3.39 6.07), and this result was seen for all age strata younger than 60. Also, for female respondents, night-mare frequency increased from ages 10–19 to 20–39 then decreased mono-tonically to ages 50–59. For male respondents, nightmare frequency was stable from ages 10–19 to 30–39 then decreased to ages 50–59. An increase in nightmare frequency was observed post-September 11 only for male respondents—particularly for 10- to 29-year-olds. This increase was sus-tained 2 years later. These effects were maintained when dream recall was held constant. Results replicate, in a single sample, previously published gender and age effects and provide new evidence that the nightmares of males may be differentially sensitive to traumatic events for which victims and/or perpetrators are primarily male.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".