[Measuring alexithymia in fibromyalgia: the need for a multimodal measurement method to replace the TAS-20].
Bibliographic record
Abstract
BACKGROUND: In an earlier publication that investigated alexithymia in fibromyalgia, we showed the Toronto Alexithymia Scale was the only instrument being used to measure alexithymia. AIM: To find out which instruments are currently available for measuring alexithymia, to compare the psychometric properties of these instruments and to decide whether some of the test methods involved should be used to give extra value to alexithymia research. METHOD: We conducted a systematic review of the literature in Medline/PubMed with a number of search terms. We selected articles relating to psychometric properties of the tests performed and decided whether they could be influenced by negative affect. RESULTS: We found that 14 different instruments were used to measure alexithymia. From our evaluation we excluded tests which had weak psychometric properties or had been inadequately assessed. There remained three observation scales and two self-report questionnaires, which had been adequately validated and whose relative strengths and weaknesses were compared. CONCLUSION: In view of these findings, we recommend that in studies of alexithymia in fibromyalgia a multimodal measurement method should be used rather than only the tas-20.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".