Who is filling what? The contrast between oral health and human health resources in Mexico
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Veney et al. (1997) made a list of inadequate approaches that have been ineffective and inefficient for many health systems in less-developed countries in their quest for better health of the population. Given the constraints imposed by limited resources, many authorities behave as if their goal were not success but rather failure. This paper argues that the Veney et al. (1997) list omitted a fundamental component: the creation of health care personnel largely irrelevant to the actual solution of the population's health problems. A symbiotic relationship between a higher educational system and a professional body can easily be established (e.g., by limiting the access to teaching positions and administration of clinical facilities in educational systems to highly specialized clinicians). Since this interaction between the sophisticated clinical systems, the modalities of professional practice, and the highly artificial educational settings supports the rationales behind each one of these three factors, the system as a whole can be detached from the reality of the population group. This arrangement usually has strong internal consistency but limited relevance to epidemiologically important challenges. There is an indissoluble link between the profile, limitations and goals of a profession and those of the educational system that underlies the profession. A critical appraisal is made of how successful the priorities within this approach to human health resources planning in dentistry can be, in terms of the number and clinical profiles of dental personnel created to solve oral health problems in the Mexican setting during the last 25 years.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it