Vulnerability and Primary Health Care: An Integrative Literature Review
Bibliographic record
Abstract
The objective was to analyze the evidence available in the scientific literature on the concept of vulnerability, in theoretical perspectives and its use, in Primary Health Care. An integrative literature review was carried out with the inclusion criteria: articles in English, full text, peerreviewed, related to vulnerability and primary health care, with the explicit concept of vulnerability, and published until July 31, 2020. The electronic databases accessed were by crossing the descriptors "vulnerability," "vulnerabilities," "primary health care," "primary healthcare," and "primary care." The final sample consisted of 19 articles. The thematic analysis produced 2 themes: "Theoretical foundations of the concept of vulnerability" and "The use of the concept of vulnerability in PHC." In the second theme, 2 sub-themes emerged: Evaluation of health policies, programs, and services and Classification of individuals, groups, and families. There was a plurality of theoretical foundations for the concept of vulnerability and a smaller scope of its use in Primary Health Care. It is expected that the study will subsidize public policymakers and health teams in the design of services and actions aimed at vulnerable populations and in situations of vulnerability.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".