Low self-esteem and psychiatric patients: Part II – The relationship between self-esteem and demographic factors and psychosocial stressors in psychiatric patients
Bibliographic record
Abstract
BACKGROUND: The objective of the present study was to identify the effects and relative importance of demographic factors and psychosocial stressors on self-esteem of psychiatric patients. METHOD: The present study was carried out on a consecutive sample of 1,190 individuals attending an open-access psychiatric outpatient clinic. Patients were diagnosed according to DSM III-R diagnostic criteria following detailed assessments. At screening, patients and controls completed two self-esteem questionnaires, the Rosenberg self-esteem scale and the Janis and Field Social Adequacy scale. In addition, a large amount of demographic and psychosocial data was collected on all patients. RESULTS: Significantly increased self-esteem was observed with an increase in age, educational achievement and income. Employed patients showed significantly higher self-esteem compared to unemployed patients. Female patients had a significantly lower self-esteem compared to male patients. The self-esteem of psychiatric patients did not vary significantly with their marital status. No relationship was detected between acute stressors and the self-esteem of psychiatric patients, although severe enduring stressors were associated with lower self-esteem in psychiatric patients. CONCLUSION: The results of this large study demonstrate that the self-esteem of adult psychiatric patients is affected by a number of demographic and psychosocial factors including age, sex, educational status, income, employment status, and enduring psychosocial stressors.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".