Relevance of Cues for Assessing Hallucinated Voice Experiences
Bibliographic record
Abstract
PURPOSE: To assess psychiatric nurses' views of the importance of itemized content represented on an Inventory of Voice Experiences (IVE) for ongoing assessment of atypical auditory sense perception in people who hear voices. METHODS: Over 6 months, 317 experienced psychiatric nurses rated 58 assessment cues for hallucinated voice experiences. Cronbach's alpha, Cohen's kappa, and Bartko's intraclass correlation coefficients were used to measure concordance of the nurses' judgments against two hypothetical standards derived for purposes of the study. FINDINGS: There was moderate support for both the internal consistency of the nurses' judgments concerning the importance of itemized content represented on the IVE and overall equivalence of the content. There was modest-to-moderate concordance of the nurses' original and subsequent judgments but a lack of concordance of the nurses' judgments with equally weighted judgments of the principal investigator even though the judgments of the investigator were based on extant literature and published reports of voice hearers. CONCLUSIONS: Results may reflect the effects of repeated testing, but it also is possible that some nurses did not have enough knowledge or professional experience to quantify judgments about the importance of hallucinated voice descriptions tied to the items on the IVE. The findings are being used to refine the IVE. PRACTICE IMPLICATIONS: Findings provide nurses with opportunities for discerning specific characteristics, antecedents, and consequences of voice hearing along with their implications for health and well-being. Discernment of this information will facilitate identification of more specific and meaningful options for helping voice hearers manage their voices.
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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 | 0.005 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".