Alexithymia in an unconventional sample of Forestry Officers: a clinical psychological study with surprising results
Bibliographic record
Abstract
Background: Clinical psychological dynamics are known as effective in the onset of medical conditions. In this regard, alexithymia represents a well-recognized and studied phenomenon, whose study is attracting academic attention. Its relations with several conditions have found consistent resonance in the clinical fields, so that the application of clinical models to disregarded populations represents a relevant clinical opportunity. The current study was aimed at extending alexithymia study to a population consisting of State Forestry officers, including significant variables as age and years of tenure. Methods: The observation group consisted of 59 State Forestry officers, aged between 48 and 67 years old (SD=3.436). All subjects fully completed the protocol, consisting of the Toronto Alexithymia Scale (TAS-20) and a sociodemographic questionnaire. Descriptive statistics, correlational analyses, dependencies and statistical differences were performed in order to let significant relations emerge. Statistical analyses were performed using SPSS 24.0 for the Window package. Results: Descriptive statistics highlighted high scores related to all alexithymia factors (Difficulty Identifying Feelings, Difficulty Describing Feelings, Externally Oriented Thinking, TAS-20 Total score) in the considered individuals. Correlational analyses provided significant relations with reference to age, years of tenure and the whole set of alexithymic variables. Significant dependencies emerged among the selected predictors (age and years of tenure) and Tas-20 variables (including total score), as well as significant differences between two selected groups (38 and 38 years of tenure). Conclusion: Alexithymia emerged as particularly present in the considered population of State Forestry officers, demonstrating its sensibility with reference to age and years of tenure variables. The study of psychological phenomena affecting general subjects’ condition represent an extension of a present research field of high innovativeness, considering the lack of knowledge referred to the selected sample. Furter studies should increase the number of included individuals in order to favor the extension of the emerged results.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".