All Goals are Equal: No Interactions Between Depressive Symptoms and Goal Characteristics on Goal Progress
Bibliographic record
Abstract
Introduction. Depression is related to poor achievement and impacts people's capacity to attain their goals (American Psychiatric Association, 2013; Johnson et al., 2010; Street, 2002). But do depressive symptoms impact goal pursuit differently depending on the kinds of goals that people pursue? Methods. Across three studies (total N = 666 undergraduate students, total goals = 2,546), we examine the role of up to 16 goal characteristics as moderators in the relationship between depressive symptoms and goal progress. Depressive symptoms and goal characteristics were assessed at baseline, and participants reported on goal progress at a follow-up 1 month (Study 1), 4 months (Study 2), or 8 months (Study 3) later. Results. The effect of depressive symptoms on goal progress was nonsignificant in two out of three studies (including one with low power), but an internal meta-analysis presented a small negative effect. Most goal characteristics did not moderate the relationship between depressive symptoms and goal progress, with Bayes factors suggesting substantial to very strong evidence in favor of the null hypotheses. Discussion. The kinds of goals students pursue may not matter in the presence of depressive symptoms. On one hand, this may provide a bleak outlook in highlighting that depressive symptoms impact all goals regardless of how well they are selected. On the other hand, the effects were small, which may offer a hopeful outlook for undergraduate students experiencing depressive symptoms, who may still be able to progress on their personal goals.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.012 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".