Bibliographic record
Abstract
As currently conceived, precision public health is at risk of becoming precision medicine at a population level. This paper outlines a framework for precision public health that, in contrast to its current operationalisation, is consistent with public health principles because it integrates factors at all levels, while illuminating social position as a fundamental determinant of health and health inequities. We review conceptual foundations of public health, outline a proposed framework for precision public health and describe its operationalisation within research and practice. Social position shapes individuals' unequal experiences of the social determinants of health. Thus, in our formulation, precision public health investigates how multiple dimensions of social position interact to confer health risk differently for precisely defined population subgroups according to the social contexts in which they are embedded, while considering relevant biological and behavioural factors. It leverages this information to uncover the precise and intersecting social structures that pattern health outcomes, and to identify actionable interventions within the social contexts of affected groups. We contend that studies informed by this framework offer greater potential to improve health than current conceptualisations of precision public health that do not address root causes. Moreover, expanding beyond master categories of social position and operationalising these categories in more precise ways across time and place can enrich public health research through greater attention to the heterogeneity of social positions, their causes and health effects, leading to the identification of points of intervention that are specific enough to be useful in reducing health inequities. Failure to attend to this level of particularity may mask the true nature of health risk, the causal mechanisms at play and appropriate interventions. Conceptualised thus, precision public health is a research endeavour with much to offer by way of understanding and intervening on the causes of poor health and health inequities.As currently conceived, precision public health is at risk of becoming precision medicine at a population level. This paper outlines a framework for precision public health that, in contrast to its current operationalization, is consistent with public health principles because it integrates factors at all levels, while illuminating social position as a fundamental determinant of health and health inequities. We review conceptual foundations of public health, outline a proposed framework for precision public health and describe its operationalization within research and practice. Social position shapes individuals' unequal experiences of the social determinants of health. Thus, in our formulation, precision public health investigates how multiple dimensions of social position interact to confer health risk differently for precisely defined population subgroups according to the social contexts in which they are embedded, while considering relevant biological and behavioural factors. It leverages this information to uncover the precise and intersecting social structures that pattern health outcomes, and to identify actionable interventions within the social contexts of affected groups. We contend that studies informed by this framework offer greater potential to improve health than current conceptualizations of precision public health that do not address root causes. Moreover, expanding beyond master categories of social position and operationalizing these categories in more precise ways across time and place can enrich public health research through greater attention to the heterogeneity of social positions, their causes and health effects, leading to identification of points of intervention that are specific enough to be useful in reducing health inequities. Failure to attend to this level of particularity may mask the true nature of health risk, the causal mechanisms at play and appropriate interventions. Conceptualized thus, precision public health is a research endeavour with much to offer by way of understanding and intervening on the causes of poor health and health inequities.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".