Bibliographic record
Abstract
Ispitivanje psiholoških faktora u podlozi kronične boli Exploring the psyhological factors underlying chronic pain Alen Serhatlić SAŽETAK Svrha ovog istraživanja bila je ispitati ulogu različitih psiholoških faktora, točnije osobina ličnosti, strategija suočavanja i zdravstvenog lokusa kontrole, u procjeni intenziteta boli kod osoba koje pate od kronične boli te utvrditi jesu li različite strategije suočavanja s boli medijatori u odnosu između osobina ličnosti i faktora zdravstvenog lokusa kontrole s jedne te percipiranog intenziteta boli s druge strane.Istraživanje je provedeno na uzorku od 292 sudionika koji pate od kronične boli.Oni su ispunili set upitnika koji su ispitivali osobine ličnosti (IPIP50, podljestvice neuroticizma i ekstraverzije), zdravstveni lokus kontrole (MHLC), strategije suočavanja s boli (CSQ-24) te percipirani intenzitet boli mjeren kratkom verzijom McGill upitnika boli (SF-MPQ) i verbalnom bodovnom ljestvicom.Rezultati su pokazali kako sudionici mlađe dobi, oni koji češće uzimaju lijekove za smanjenje boli te oni koji su skloniji katastrofiranju prilikom suočavanja s njom u prosjeku doživljavaju više intenzitete boli.Katastrofiranje se pokazalo i kao značajan medijator u odnosu između neuroticizma i intenziteta boli mjerenog sa SF-MPQ te u odnosu između eksternalnog zdravstvenog lokusa kontrole (faktor snažni drugi) i intenziteta boli mjerenog verbalnom bodovnom ljestvicom.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".