The chicken or the egg? Systematic investigation of the effect of order of administration of Memory Questionnaires and Well-being Scales
Bibliographic record
Abstract
Narrative research claims that episodic/autobiographical memory characteristics and themes represent stable individual differences that relate to well-being. However, the effects of the order of administration of memory descriptions and well-being scales have never been investigated. Of importance, social cognitive research has shown that trivial contextual factors, such as completing a self-report measure, can influence the type of memories recollected afterwards and that memory recollection can transiently affect subsequent self-report ratings--both of which underscore that transient contextual effects, rather than stable individual differences in memory could be responsible for the correlation between memory characteristics and well-being. The present study examined if the order in which (positive or negative) memory and well-being scales are completed affects the characteristics and themes of the memory described, the scores of well-being reported and the relationship between the two. The results revealed some effects of order of administration when memories were described before completing well-being scales, but only on a situational measure of well-being, not on a trait measure. In sum, we recommend assessing memory-related material at the end of questionnaires to avoid potential mood-priming effects.
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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 | 0.048 | 0.173 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".