Does crying help? Development of the beliefs about crying scale (BACS)
Bibliographic record
Abstract
Crying is often considered to be a positive experience that benefits the crier, yet there is little empirical evidence to support this. Indeed, it seems that people hold a range of appraisals about their crying, and these are likely to influence the effects of crying on their emotional state. This paper reports on the development and psychometric validation of the Beliefs about Crying Scale (BACS), a new measure assessing beliefs about whether crying leads to positive or negative emotional outcomes in individual and interpersonal contexts. Using 40 preliminary items drawn from a qualitative study, an exploratory factor analysis with 202 participants (50% female; aged 18-84 years) yielded three subscales: Helpful Beliefs, Unhelpful-Individual Beliefs, and Unhelpful-Social Beliefs, explaining 60% of the variance in the data. Confirmatory factor analysis on the 14-item scale with 210 participants (71% female; aged 17-48 years) showed a good fit to the three factors. The subscales showed differential relationships with measures of personality traits, crying proneness, emotion regulation and expressivity, and emotional identification (alexithymia). The BACS provides a nuanced understanding of beliefs about crying in different contexts and helps to explain why crying behaviour may not always represent positive emotion regulation for the crier.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".