Alexithymia and illness perceptions in persons with multiple sclerosis and their partners: : A preliminary study
Bibliographic record
Abstract
Background: Illness perceptions encompass opinions regarding the nature, severity and curability of a disease, that can influence the adjustment to and management of chronic illnesses. Despite their importance, little is known about what may influence the formation of illness appraisals. Aims: My literature review aimed to synthesise and critically appraise quantitative evidence regarding the relationship between illness perceptions (among persons with multiple sclerosis and their caregivers) and outcome measures. My empirical study tested alexithymic traits as possibly associated with illness perceptions among persons with multiple sclerosis and their partners, as well as within the dyads composed of persons with multiple sclerosis and partners. Methods: A systematic literature search was conducted on MEDLINE, PsycINFO, and CINAHL. Twenty papers were selected, quality appraised and synthesized using a narrative approach. A crosssectional study was conducted on persons with multiple sclerosis and their partners, who completed a battery of questionnaires, including the Toronto Alexithymia Scale-20 (to assess alexithymic traits) and the Illness Perception Questionnaire Revised (to investigate the participants’ perceptions about multiple sclerosis). A dyadic data analysis (Actor-Partner Interdependence Model) was performed in order to test the effect of alexithymic traits both on a person’s own illness perceptions (actor effect) and on the partner’s illness perceptions (partner effect). Findings: The findings of my literature review indicate a link between negative illness perceptions (e.g. more severe consequences) and worse psychophysical outcomes (e.g. fatigue). The results of the empirical study indicate that alexithymic traits may be associated with some illness perceptions both at the individual and dyadic level, whereby higher alexithymic traits relate to more negative illness perceptions. Conclusion: My literature review highlights the clinical importance of illness perceptions. The findings of the cross-sectional research, reporting a relationship between alexithymia and more negative illness appraisals, may inform therapeutic interventions aimed at reducing alexithymic traits, which in turn may reduce negative, and potentially dysfunctional, illness perceptions. perceptions.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".