Accuracy of nurses' perceptions of voice hearing and psychiatric symptoms
Bibliographic record
Abstract
AIM: This article reports a study of nurses' assessments of a voice hearer's voices and psychiatric symptoms, and associations of these perceptions with nurses' education, career experience and primary care role. BACKGROUND: Traditional views in nursing suggest that to engage voice hearers in a discussion of their voices is to support the psychopathology of the voice hearers. Research into how voice hearers conceptualize voice hearing has generated a range of perspectives, raising concerns about whether nurses capture sufficient, accurate and specific assessment data about the experiences of voice hearers. METHOD: One hundred and fifteen psychiatric nurses rated items on an Inventory of Voice Experiences and the Brief Psychiatric Rating Scale while viewing a videotaped assessment of a voice hearer with serious and persistent mental illness. The voice hearer in the videotape used the same instruments to rate his own voices and symptoms. This self-assessment was undertaken 30 days before and immediately before production of the video-recorded assessment. The data were collected between 2000 and 2002. RESULTS: The voice hearer's ratings of his voice hearing experiences and psychiatric symptoms were consistent over a 30-day period. Most nurse ratings of the voice hearer's voices and psychiatric symptoms did not match those of the voice hearer. However, the voice hearer and nurses demonstrated a moderately positive association between the voice hearer's voices and symptoms. Ratings of graduate-educated case managers and clinical nurse specialists (n=30) in clinical practice settings were more consistent with one another and corresponded more closely with the ratings of the voice hearer, particularly for the association between the voice hearer's voices and symptoms. CONCLUSION: Accurate and specific assessment of voice hearing may facilitate engagement with voice hearers and improve the selection of strategies to help them manage the voices that upset them.
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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 | 0.004 | 0.061 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".