Computer passwords as a timely booster for writing-based psychological interventions
Bibliographic record
Abstract
Writing-based psychological interventions have been widely implemented to produce adaptive change, e.g., through self-affirmation (reminding people of their most important values). To maintain the long-term effects of these interventions, we developed a form of intervention boosters-using user-customized computer passwords to convey the therapeutic messages. We examined whether computer passwords could enhance the effect of a self-affirmation intervention on the psychological well-being of sexual minority undergraduate students as they begin university. Participants were randomly assigned to either complete a self-affirmation writing exercise and create a self-affirming computer password to use for 6 weeks or complete a control writing exercise and create a control computer password. We found that frequency of password usage moderated the intervention effect, such that frequent use of self-affirming passwords buffered decreases in psychological well-being over the study period. These findings suggest that passwords can serve as a low-cost, low-burden, and timely booster for writing-based psychological interventions.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".