Patient complexity assessment tools containing inquiry domains important for Indigenous patient care: A scoping review
Bibliographic record
Abstract
Patient complexity assessment tools (PCATs) are utilized to collect vital information to effectively deliver care to patients with complexity. Indigenous patients are viewed in the clinical setting as having complex health needs, but there is no existing PCAT developed for use with Indigenous patients, although general population PCATs may contain relevant content. Our objective was to identify PCATs that include the inquiry of domains relevant in the care of Indigenous patients with complexity. A scoping review was performed on articles published between 2016 and 2021 to extend a previous scoping review of PCATs. Data extraction from existing frameworks focused on domains of social realities relevant to the care of Indigenous patients. The search resulted in 1078 articles, 82 underwent full-text review, and 9 new tools were identified. Combined with previously known and identified PCATs, only 6 items from 5 tools tangentially addressed the domains of social realities relevant to Indigenous patients. This scoping review identifies a major gap in the utility and capacity of PCATs to address the realities of Indigenous patients. Future research should focus on developing tools to address the needs of Indigenous patients and improve health outcomes.
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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 | 0.034 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".