Tengsl tilfinningaólæsis, líkamsvitundar og leiða við húðkroppunaráráttu
Bibliographic record
Abstract
Húðkroppunarárátta einkennist af sífelldu kroppi í húð sem veldur sárum. Getur hún valdið fólki mikilli vanlíðan. Tilfinningaólæsi (alexithymia) felst í því að eiga erfitt með að bera kennsl á tilfinningar sínar og einblína á ytra áreiti og líkamlegar kenndir fremur en eigið hugarástand. Kannað var hvort tengsl væru milli tilfinningaólæsis, líkamsvitundar (somatic awareness), leiða (boredom) og húðkroppunaráráttu. Tilgátan var sú að tilfinningaólæsi gæti spáð fyrir um húðkroppunaráráttu og að líkamsvitund annars vegar, og leiði hins vegar, væru þar millibreytur. Þátttakendur voru 337 nemendur við Háskóla Íslands og svöruðu þeir spurningalistum um húðkropp, (Skin Picking Scale), kvíða og þunglyndi (HADS), líkamsvitund (Pennebaker Inventory of Limbid Languidness), leiða (Boredom Proneness Scale) og tilfinningaólæsi (Toronto Alexithymia Scale, TAS – 20). Í ljós kom að tilfinningaólæsi spáir fyrir um húðkroppunaráráttu, jafnvel þegar stjórnað er fyrir áhrif kvíða og þunglyndis og fékk tilgátan um að líkamsvitund væri þar millibreyta stuðning. Tilgátan um að leiði væri millibreyta í sambandi tilfinningaólæsis við húðkropp fékk ekki stuðning en þessi tengsl þarf að kanna nánar.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.010 |
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".