Investigating Goal Difficulty and Motivational Quality as Moderators of the Association Between Sleep Quality and Goal Progress
Bibliographic record
Abstract
People often struggle to make progress on their personal goals following a night of poor sleep. But does this association depend on certain characteristics of the goals themselves? This paper examines whether the association between poor sleep quality and lower next day goal progress depends on the difficulty of the goal or the motivation quality (want-to, have-to). These objectives were carried out in two longitudinal studies. The first study (N = 361) examined whether community adults’ morning reports of sleep quality were related to the progress they made on several of their goals over the course of a single day. The second study instead tracked university students’ (N = 156) sleep quality and goal pursuit over a seven-day period. The findings from both studies suggested that participants in the community adult (but not university student) sample who slept poorly the night before tended to make less progress on their goals the following day. The relation between sleep quality and goal progress also did not depend on goal difficulty or motivation quality, as confirmed by Bayesian analyses showing moderate to strong evidence for the null. Overall, these findings highlight that a night of poor sleep quality may only be detrimental to goal progress in certain situations, and this association does not depend on the difficulty of the goals that people are pursuing or their motivation for achieving them.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".