Embracing the population health framework in nursing research
Bibliographic record
Abstract
Individuals' health outcomes are influenced not only by their knowledge and behavior, but also by complex social, political and economic forces. Attention to these multi-level factors is necessary to accurately and comprehensively understand and intervene to improve human health. The population health framework is a valuable conceptual framework to guide nurse researchers in identifying and targeting the broad range of determinants of health. However, attention to the intermediate processes linking multi-level factors and use of appropriate multi-level theory and research methodology is critical to utilizing the framework effectively. Nurse researchers are well equipped to undertake such investigations but need to consider a number of political, societal, professional and organizational barriers to do so. By fully embracing the population health framework, nurse researchers have the opportunity to explore the multi-level influences on health and to develop, implement and evaluate interventions that target immediate needs, more distal factors and the intermediate processes that connect them.
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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 | 0.194 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.005 | 0.056 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".