Early release of selected estimates based on data from the 2008 national health interview survey,” National Center for Health Statistics
Bibliographic record
Abstract
Release measures are being published prior to final data editing and final weighting to provide access to the most recent information from NHIS. The estimates will be updated as each new quarter of NHIS data becomes available. Two additional periodical reports are published through the Early Release Program. Health Insurance Coverage: Early Release of Estimates From the National Health Interview Survey (1) is published quarterly and provides additional estimates of health insurance coverage. Wireless Substitution: Early Release of Estimates From the National Health Interview Survey (2) is published in June and December and provides selected estimates of telephone coverage. Other Early Release reports and tabulations on special topics are released as needed. In addition to these reports, preliminary microdata files containing selected NHIS variables are produced as part of the Early Release Program. For each data collection year, these files are made available three times: in about September and December during the data collection year, and in about March following the data collection year. NHIS data users can analyze these files through the NCHS Research Data Center without having to wait for the final annual NHIS microdata files to be released in about June following the end of the data collection year. The 15 measures included in the present report are lack of health insurance coverage and type of coverage, having a usual place to go for medical care, obtaining needed medical care, receipt of influenza vaccination, receipt of pneumococcal vaccination, obesity, leisure-time physical activity, current cigarette smoking, alcohol
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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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.077 | 0.080 |
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".