The Use of Defenses and Physician Health Care Costs: Are Physician Health Care Costs Lower in Persons with More Adaptive Defense Profiles?
Bibliographic record
Abstract
BACKGROUND: The objective of the present study was to determine if persons who use more adaptive defenses have lower physician health care costs compared to those who use less adaptive defenses. METHODS: We randomly selected 667 persons from the 1995 population-based Nova Scotia Health Survey who completed a videotaped structured interview. Each interview was rated for typical defense use by the Defense-Q. We obtained physician health care costs for 3 months before and after the interview, as well as medical diagnoses and measures of psychological functioning. RESULTS: A more adaptive defense profile significantly predicted lower future physician health care costs. These results were found when controlling for other psychosocial variables, before and after controlling for previous physician health care costs, and when testing only within a physically healthy subsample. Results of secondary analyses showed that a more adaptive defense profile was positively related to a number of psychosocial variables, such as nurse's rating of competence, lack of depressive symptoms, and days at work. CONCLUSIONS: The adaptiveness of a person's defense use in managing affect is important in predicting physician health care costs as well as psychosocial functioning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".