Influences of inattention on perceived self-efficacy, stress, and depression
Bibliographic record
Abstract
Introduction Going through life mindlessly appears to produce feelings of boredom and depression, suggesting that cognitive deficits can lead both directly and indirectly to emotional problems. Under this hypothesis, there are numerous possible routes from attention to affective issues, including through the sense of self-efficacy – a judgement about our ability to successfully achieve goals – and the experience of psychological stress. The present study assessed potential pathways from everyday inattentiveness, through the presumed intermediate experiences of diminished self-efficacy and psychological stress, to the experience of depressive affect. Method In two studies we collected questionnaire responses from large groups of participants (N = 188, Study 1; N = 399, Study 2), assessing individual differences in trait inattention, attention-related errors, self-efficacy, stress, and depression. Results Via path analyses we tested several predictions: 1) the frequency of attention lapses predicts depressive affect; 2) attention-related errors mediate effects of inattention in predicting both self-efficacy and stress; 3) self-efficacy and stress directly influence depressive affect. The results indicate the proposed Attention-to-Affect models provide good fit overall. They also indicate a reversal of the causal flow, while consistent with traditional views, does not adequately fit the data. Discussion That the Attention-to-Affect models provide good fit for the data is consistent with the view that everyday inattention contributes to the emotional distress that creates depression. While this view is contrary to the typical conception of attention problems as consequences of depression, it is consistent with our own previous findings. Accordingly, our results continue to suggest it is important for future research to further validate this pathway and to consider directly remedying attentional issues as a potentially important part of depression treatment strategies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".