Barreras para el desarrollo de investigación en medicina familiar en Iberoamérica: Revisión sistemática
Bibliographic record
Abstract
Objective: To identify main barriers for the development of research in family medicine in Iberoamerica from articles published in Spanish. Methods: A systematic review was performed according to PRISMA 2020. Original articles were selected, published until October 2021, which addressed existing barriers to the development of research in family medicine. The review was carried out through ScienceDirect, Scielo, and Google Scholar databases. Article's classification was made according to the locality, type of study, sample size, and main barriers detected. The quality of articles that met the selection criteria was assessed according to the Newcastle-Ottawa and CASP checklists. Results: 207 articles were identified, nine met the selection criteria. Most of the studies identified lack of training in research methodology, time, institutional support, incentives, and trained tutors as the main barriers to research development. Conclusions: Common barriers and limitations were identified in the analyzed studies, which focused on the lack of training for students and teachers to develop research, as well as the lack of time and institutional support, among others. Comprehensive strategies are required to mitigate the effect of the barriers detected to strengthen research in family medicine.
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 | 0.155 | 0.189 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.051 | 0.029 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".