If we cannot measure it, we cannot improve it: Understanding measurement problems in routine oral/dental assessments in Canadian nursing homes—Part II
Bibliographic record
Abstract
Abstract Objective To evaluate the response process validity of the Resident Assessment Instrument—Minimum Data Set 2.0 (RAI) oral/dental items and the organisational processes for assessing nursing home (NH) residents’ oral/dental status. Background Although care aides provide most direct care to NH residents, including oral care, they are not directly involved in structured care planning activities, including RAI assessments. This most likely affects the accuracy of RAI assessments, as well quality of care. However, we neither know how well regulated and unregulated care staff understand the RAI oral/dental items, nor what processes are used in completing oral/dental assessments. Methods We conducted nine focus groups with 44 care aides, nurses, allied health providers, clinical specialists and managers. We discussed randomly selected RAI oral/dental assessments with focus group participants, including participants’ understanding of the items and why the options were selected. Participants also explained the communication and process for completing the RAI. Results Participants’ perceptions of the oral/dental items aligned fairly well with the item definitions. However, responses primarily focused on severe oral/dental problems with obvious physical characteristics (eg black teeth denoting caries). For non‐visual oral problems, such as pain, staff relied on resident verbalisation. No formal mechanisms were described for care aides to update nurses on residents’ oral health needs. Conclusions Performance problems of RAI oral/dental items are largely rooted in poor communication between care aides and nurses and not integrating care aides in assessment processes. We need policies that address these problems in order to improve NH residents’ poor oral health.
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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 | 0.060 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".