Detecting nurse‐perceived patient treatment difficulty of psychiatric patients in hospital: an evaluation of a patient assessment sheet
Bibliographic record
Abstract
Many changes have occurred in hospital psychiatric care, including decreased length of hospital stay and increased patient acuity. These changes highlight the need for nurses to adequately assess and formally document patient treatment difficulties. The purposes of this study were to determine the ability of the Patient Assessment Sheet (PAS) to predict patient 'problems' that psychiatric nurses perceived as associated with patient treatment difficulty, and to identify the patient problems missing from the PAS. These purposes were accomplished by comparing the PAS to the Hospital Treatment Rating Scale (HTRS). A correlational design and multiple linear regression technique were used. Eight psychiatric registered nurses assessed a total of 110 patients, admitted consecutively to one inpatient psychiatric unit. The HTRS and the PAS were used independently for each patient. Four PAS items (active affect, passive affect, aggression toward self, and patient confusion) significantly predicted 38% of the variance from the HTRS; and three HTRS items (isolation and withdrawal from relationships, noninvolvement in treatment, and wide variability in mood) significantly predicted 22% of the residual variance from the HTRS. The identified PAS and HTRS items help to make visible patient problems associated with nurse-perceived patient treatment difficulty. This identification is potentially important for both clinical and political purposes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".