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Record W6891244734 · doi:10.3886/icpsr20541.v9

National Social Life, Health, and Aging Project (NSHAP): Round 1, [United States], 2005-2006

2019· dataset· en· W6891244734 on OpenAlexaff

Bibliographic record

VenueICPSR Data Holdings · 2019
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMental healthSocial supportReproductive healthPublic healthFocus groupSocial environmentPhysical health

Abstract

fetched live from OpenAlex

The National Social Life, Health and Aging Project (NSHAP) is the first population-based study of health and social factors on a national scale, aiming to understand the well-being of older, community-dwelling Americans by examining the interactions among physical health, illness, medication use, cognitive function, emotional health, sensory function, health behaviors, and social connectedness. It is designed to provide health providers, policy makers, and individuals with useful information and insights into these factors, particularly on social and intimate relationships. The National Opinion Research Center (NORC), along with Principal Investigators at the University of Chicago, conducted more than 3,000 interviews during 2005 and 2006 with a nationally representative sample of adults aged 57 to 85. Face-to-face interviews and biomeasure collection took place in respondents' homes. The following files constitute Round 1: Core Data, Marital/Cohabiting History Data, Social Networks Data, Medications Data, and Sexual Partners Data. Included in the Core file (Dataset 1) are demographic characteristics, such as gender, age, education, race, and ethnicity. Other topics covered respondents' social networks, social and cultural activity, physical and mental health including cognition, well-being, illness, medications and alternative therapies, history of sexual and intimate partnerships and patient-physician communication, in addition to bereavement items. In addition data was collected from respondents on the following items and modules: social activity items, physical contact module, sexual interest module, get up and go assessment of physical function and a panel of biomeasures including, weight, waist circumference, height, blood pressure, smell, saliva collection, taste, and a self-administered vaginal swab for female respondents. The Core file also contains a count of the total number of drugs taken, and a variable for each observed therapeutic category, indicating whether the respondent reported taking one or more medications in that category. These variables are derived from the information in the medications file, and thus are guaranteed to be consistent with it. The Marital/Cohabiting History file (Dataset 2) contains one record for each marriage or cohabitation identified in Section 3A of the questionnaire. The Social Networks file (Dataset 3) contains one record for each person identified on the network roster. Respondents who refused to participate in the roster or who did not identify anyone are not represented in this file. The Medications file (Dataset 4) contains one record for each item listed in the medications log (including alternative medicines and nutritional products). Respondents who did not report taking any medications or who refused to participate in this module are not represented in this file. Lastly, the Sexual Partners file (Dataset 5) contains one record for each sexual partner identified in Section 3A of the questionnaire. NACDA also maintains a Colectica portal with the NSHAP Core data across rounds 1-3, which allows users to interact with variables across rounds and create customized subsets. Registration is required.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.228
Threshold uncertainty score0.453

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.005
Science and technology studies0.0020.000
Scholarly communication0.0030.002
Open science0.0040.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0120.008

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.

Opus teacher head0.127
GPT teacher head0.387
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreDataset

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".

Quick stats

Citations11
Published2019
Admission routes1
Has abstractyes

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Same venueICPSR Data HoldingsFrench-language works237,207