(In)Formal Support and Unmet Needs in The National Long-Term Care Survey
Bibliographic record
Abstract
We linked individual-level data from the 2004 wave of the National Long-Term Care Survey with state-level data from the National Aging Program Information Systems (NAPIS) State Program Reports to predict care mix and unmet need for assistance. Our sample consisted of 2422 community-dwelling individuals aged 65 and older (69% women, 8% nonwhite) who reported at least one limitation in an instrumental or basic activity of daily living. We used the data to predict the mix of formal and informal support received, and the probability of having at least one unmet need from individual (predisposing, enabling, and need) characteristics with statelevel home help coverage rates, intensity of home help services, and proportion of population aged 60+ residing in institutional settings. Consistent with past research, a majority (52.6%) of the disabled sample reported unmet need. At the individual level, enabling (availability of kin support) and need (number of basic and instrumental activity of daily living impairments, BADLs and IADLs) were most strongly associated with care mix and unmet need. Statelevel services were not associated with receipt of informal supports. In states providing home help services to a higher proportion of elders, women were more likely to receive formal help. In states providing more intensive services, women were less likely and individuals living alone more likely to receive formal supports. In states where a higher proportion of elders lived in nursing homes, individuals living alone were more likely to receive formal assistance, less likely overall to report unmet needs, but the oldest-old were more likely to report unmet need.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".