Implementation of the interprofessional patient care needs assessment tool in 12 acute-care inpatient units
Bibliographic record
Abstract
The novel Interprofessional Patient Care Needs Assessment Tool (IPPCNAT) was designed to characterize unit level patient care needs from a health disciplines lens. This work describes the implementation of the IPPCNAT at an acute care inpatient hospital in Toronto, Canada. The IPPCNAT was completed by health discipline clinicians from 12 in-scope units. Clinicians were invited to a computer lab during the assessment days to complete the spreadsheet-based tool and findings were compiled into an interactive dashboard. The IPPCNAT was completed by 94 clinicians representing six disciplines, capturing data on 360 unique patients at one time point. The IPPCNAT summarized unit staffing levels, factors complicating patient care delivery, acuity of patient care needs, types of patient care needs on the unit, possible unmet care needs from the health discipline perspective, and why needs were not being met. The data enabled conversation about potential gaps in staffing, mitigation opportunities, and opportunities for budget investment. Implementation of the IPPCNAT is feasible and effective in acute care settings. The tool can provide a description of patient care needs from an interprofessional lens and help identify gaps and opportunities for optimization of staffing models.
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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".