Head injury and alexithymia: implications for family practice care
Bibliographic record
Abstract
BACKGROUND: Alexithymia, a deficit in emotional information processing, and a history of head injury have both been found to be related to high rates of psychosomatic illness, substance abuse, depression, and utilization of primary care services. To date, no study has examined the potential comorbidity of alexithymia and head injury in a family practice setting, a necessary step in evaluating the aetiologic role of head injury in the development of alexithymia. The goals of this study are to establish prevalence of head injury and alexithymia in a family practice setting and to evaluate the relation, if present, between the two. METHODS: Patients (n =135) of a family practice residency facility were screened using the Traumatic Brain Injury Questionnaire and the Toronto Alexithymia Scale-20. RESULTS: Forty-nine per cent of the participants reported a history of head injury and 18% were alexithymic. Those with a history of head injury had significantly higher scores of alexithymia. Chi-square analysis indicated a relation between head injury and alexithymia. CONCLUSIONS: The high rates of self-reported history of head injury in family practice settings, particularly in the context of alexithymia, may adversely affect a physician's ability to care for these patients. Increasing physicians' awareness of head injury and the potential mediating role of alexithymia in medical and psychological illness may facilitate effective diagnosis and patient-physician communication.
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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 | 0.001 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".