Training programmes and mealtime assistance may improve eating performance for elderly long-term care residents with dementia
Bibliographic record
Abstract
Commentary on: Liu W, Galik E, Boltz M, et al. Optimizing eating performance for older adults with dementia living in long-term care: a systematic review. Worldviews Evid Based Nurs 2015;12:228–35.[OpenUrl][1][CrossRef][2][PubMed][3] Ability to eat autonomously at mealtimes enhances social contact and interaction, supports adequate nutrition and intake, and promotes the enjoyment of food. Yet more than half of older adults with dementia living … [1]: {openurl}?query=rft.jtitle%253DWorldviews%2BEvid%2BBased%2BNurs%26rft.volume%253D12%26rft.spage%253D228%26rft_id%253Dinfo%253Adoi%252F10.1111%252Fwvn.12100%26rft_id%253Dinfo%253Apmid%252F26122316%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1111/wvn.12100&link_type=DOI [3]: /lookup/external-ref?access_num=26122316&link_type=MED&atom=%2Febnurs%2F19%2F1%2F32.atom
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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 | 0.004 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".