Nurse judgments of hallucinated voice descriptions: relevance for intervention.
Bibliographic record
Abstract
The aim of the research was to explore the importance of 58 items of descriptive content represented on two parallel forms of an Inventory of Voice Experiences (IVE). A convenience sample of 317 well-educated psychiatric nurses rated descriptions of IVE item content three times over a period of six months according to how the content might relate to the selection of interventions for the management of verbal auditory hallucinations. Cronbach's alpha, Cohen's kappa, Pearson's r, and Bartko's intra-class correlation coefficients were used to measure the internal consistency of the nurses' judgments as well as the concordance of the judgments with a pre-selected standard. Findings from the study revealed modest-to-moderate support for internal consistency and overall equivalence of parallel item content represented on both forms of the IVE within and across three waves of data collection. Also revealed was a relative lack of concordance of the nurses' judgments with the pre-selected standard, and modest-but-consistent concordance of the nurses' original and subsequent judgments. Eight parallel items represented on the IVE demonstrated potential to serve as important cues for making decisions about intervention. This information shall be used to standardize the language and response categories of items tied to the IVE to permit more-definitive decisions about the management of verbal auditory hallucinations.
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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 | 0.009 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".