Bibliographic record
Abstract
The link between socioeconomic status and health, including oral health, is well established. The conventional measures of socioeconomic status used in these studies, such as social class and household income, have a number of weaknesses so that alternatives, in the form of area-based measures of deprivation, are increasingly being used. This paper reviews epidemiological research linking deprivation and oral health. Four types of study are identified and described: simple descriptive, comparative, analytic and explanatory. These studies confirm that deprivation indices are sensitive to variations in oral health and oral health behaviours and can be used to identify small areas with high levels of need for dental treatment and oral health promotion services. As such, they are likely to provide a useful administrative tool. In terms of research, the studies demonstrate that these measures provide a ready way of controlling for socioeconomic status in studies examining the association between oral health and other variables. However, this research, in largely replicating previous studies using social class, does not address fundamental issues concerning the mechanisms which link social inequality and health. Deprivation measures have a major role to play in research that examines features of people and places, and how they promote and/or damage both oral and general health.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".