The predictive relationship of perfectionism and alexithymia towards depression and anxiety
Bibliographic record
Abstract
Diagnoses of depression and anxiety are rising and perfectionism and alexithymia are known \nrisk factors for these disorders. However, the extent to which perfectionism and alexithymia \nmay predict these disorders has not yet been studied. Therefore, this research aims to \ninvestigate the extent to which high scores on these variables explain variance in depression \nand anxiety. Through an online survey using a non-clinical sample, 212 participants were \nrecruited. They were asked to complete three questionnaires: The Multidimensional \nPerfectionism Scale (MPS-F), The Toronto Alexithymia Scale (TAS), and the Hospital \nAnxiety and Depression Scale (HADS). Perfectionism scores accounted for 16% of the \nvariance in depression, and 29% variance in anxiety. When alexithymia was included in the \nmodel, variance explained increased to 26% and 35%, thus supporting the hypothesis that \nperfectionism, when symptoms of alexithymia are also present increases the variance in \ndepression and anxiety. These findings have enabled further understanding of these \npredictive disorders, suggesting that those higher in perfectionism and alexithymia may be at \na higher risk of depression and anxiety. This study provides implications for future \ninterventions for anxiety and depression. Targeting the identification and expression of \nemotions as well as managing and setting realistic expectations and standards would be \nsuggested, based on the findings that alexithymia increases the predictive variance for \ndepression and anxiety.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".