No perfect sleep! A systematic review of the link between multidimensional perfectionism and sleep disturbance
Bibliographic record
Abstract
The view that perfectionists are prone to experiencing sleep disturbance is widely held. Yet, almost three decades of empirical research have yielded conflicting results. Whereas some researchers viewed perfectionism as a risk factor for sleep disturbance, others spoke of "adaptive" or "positive" forms of perfectionism in the context of sleep. The multidimensional conceptualisation of perfectionism may resolve this disagreement. Thus, this systematic review aimed to clarify the perfectionism-sleep disturbance link using the widely accepted two-dimensional perfectionism model, differentiating perfectionistic concerns (defined by worries over imperfections) and perfectionistic strivings (defined by excessively high personal standards). A systematic literature search returned 24 relevant empirical studies. Perfectionistic concerns were robustly linked to sleep disturbance. Perfectionistic strivings displayed comparatively small and inconsistent relations with sleep disturbance. Finally, cross-sectional mediation studies suggested that psychological distress and dysfunctional cognitive processes might underlie the perfectionistic concerns-sleep disturbance link. These findings show that considering perfectionistic concerns in explaining, predicting, and treating sleep disturbance may be a promising approach. In contrast, perfectionistic strivings appeared neither universally adaptive nor maladaptive. We identified several critical gaps in the empirical literature and point towards future research directions, highlighting the need for more longitudinal studies.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".