If we cannot measure it, we cannot improve it: Understanding measurement problems in routine oral/dental assessments in Canadian nursing homes—Part I
Bibliographic record
Abstract
OBJECTIVE: To compare Resident Assessment Instrument-Minimum Data Set 2.0 (RAI) oral/dental items collected by nursing home (NH) care staff to (a) assessments collected by trained research assistants (RAs) and (b) "gold standard" clinical assessments by dental hygienists (DHs). BACKGROUND: Routine collection of RAI oral/dental items is mandatory in most Canadian NHs. However, the performance of these items is less than optimal and oral/dental problems are severely under-reported. Accurate assessment is a prerequisite for preventing, detecting and treating oral health problems. Not knowing the reasons for performance problems is a barrier to improving performance of the RAI oral/dental items. MATERIALS AND METHODS: We included 103 NH residents from 4 NHs in Edmonton, Alberta, Canada. Using Kappa statistics, we compared the agreement of residents' last (no older than 90 days) RAI assessment with RAI assessments completed by trained RAs and "gold standard" clinical assessments by DHs. We also assessed the inter-rater reliability (IRR) of RA and DH assessments. RESULTS: Care staff assessments had poor agreement with RA and DH assessments (Kappa < 0.2 for most items). RAs and DHs identified more oral/dental problems than care staff. However, IRR of RA assessments was low (Kappa < 0.7 for 7/9 items). IRR of DH assessments was acceptable (Kappa > 0.7) for most items. CONCLUSIONS: The quality of RAI oral/dental assessments can be improved by better training care staff and ensuring appropriate time to do the assessments. However, remaining problems-even with trained RAs-suggest that rewording some of the items or supplementing them by more robust tools may be required.
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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 | 0.043 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".