Bibliographic record
Abstract
Welcome to this bumper issue of Perspectives where we consider public health from a global aspect. As the world population undergoes demographic, social, economic and epidemiological change, challenges facing each country in some cases are the same, but in some, they are specific and unique to that population. By providing a kaleidoscope of case studies of local evidence from around the world, scenarios are presented where there is opportunity to learn from others. The papers published here help us achieve a better understanding of the external environment in which we are working and really speak to the core aspects of the Royal Society for Public Health in the commitment to improve public health and make a difference to society as a whole. We are fortunate to welcome submissions from the United Kingdom, India, Australia, Spain, Canada, Malawi, United States and Bangladesh, and we thank Dr Eugenia Cronin for recruiting such a rich international resource and for guest editing this special issue.While most countries are following a strategy of inclusivity for health care and reducing social inequalities as in Canada, it is interesting to note that recent reforms in Spain specifically exclude a portion of the population; irregular immigrants and even some Spanish citizens will only have limited access to health services, presenting a new challenge to public health professionals. Added to this is the fragmented nature of a decentralized structure of public health and the lack of a national public health workforce as there are no national databases or registries. These emerging challenges in Spain necessitate a reappraisal of the organization of public health professionals, the preparation of public health workers as well as the development of workforce planning tools to assure that future needs can be met. There is a somewhat similar situation in the United States where public health is a federated enterprise that includes national entities such as the Centers for Disease Control and Prevention, the Food and Drug Administration, state and local governmental health departments and non-governmental organizations at the national, state and community levels. These are organized around single or multiple health-related issues but make for an uncoordinated national public health workforce response.Notwithstanding, similarities are also seen in the United Kingdom where restructure has complicated the professional and career landscape by introducing a panoply of different employers, such as the 152 local authorities across England and many organizations from the voluntary sector, each with their own employment structures, needs and priorities. However, a solution has been proposed in the form of a 'skills passport' as recognition of the need for a tool to help employees navigate and plan careers in, and across, this complex environment. Skills passports are a record of a person's training, education and vocational experience, usually held on a central hosted website, offering a structure and mechanism for personal career and workforce development. …
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.019 | 0.018 |
| Insufficient payload (model declined to judge) | 0.396 | 0.251 |
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".