Personality traits of alexithymia and perfectionism in impaired awareness of hypoglycemia in adults with type 1 diabetes – An exploratory study
Bibliographic record
Abstract
Severe hypoglycemia complicates insulin therapy for type 1 diabetes, with impaired awareness of hypoglycemia (IAH) being a major risk factor. We explored associations between the personality traits, alexithymia and perfectionism, and cognitive barriers to hypoglycemia avoidance described in IAH, and evaluated their prevalence in people with and without IAH. Cross-sectional exploratory study. Ninety adults with type 1 diabetes, 54 hypoglycemia aware and 36 with IAH, completed validated questionnaires exploring alexithymia (Total Alexithymia Scale [TAS-20]) and perfectionism (Frost Multidimensional Perfectionism Scale [FMPS]); and cognitive barriers related to hypoglycemia avoidance (Attitudes to Awareness Questionnaire [A2A]. Alexithymia and perfectionism scores correlated positively with cognitive barriers associated with IAH. Specifically, alexthymia scores correlated with the ‘Hyperglycaemia Avoidance Prioritised’ factor (r = 0.265; p = .02, n = 77) and the ‘Asymptomatic Hypoglycemia Normalised’ factor (r = 0.252–0.255; p = .03, n = 77). Perfectionism scores correlated with the ‘Hyperglycaemia Avoidance Prioritised’ factor (r = 0.525; p < .001, n = 66). Overall, IAH participants were significantly more likely to score at the high end for alexithymia (17.6% vs. 1.9%, p = .008, n = 87) and at the extreme ends (high and low) for perfectionism (69.0% vs. 40.0%, χ2 (1) = 6.24, p = .01, n = 77). These novel data showing associations between alexithymia and perfectionism scores and maladaptive health beliefs in IAH suggest the intriguing possibility that personality traits may contribute to the risk of IAH, perhaps through their influence on incentives to avoid hypoglycemia. If confirmed, measuring such traits may help tailor early adjunctive psychological intervention to reduce hypoglycemia burden for people with IAH.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".