Mentorship and how to conduct research: A research primer for low- and middle-income countries
Bibliographic record
Abstract
Development of a successful research program can seem daunting when looked at from the starting line. It will take years if not decades to succeed and become sustainable. It requires local partnerships and mentoring; it mandates the establishment of review boards; it requires national health policies to allow for protected time for research in salaries and for fund granting agencies to be set up; it requires training of researchers and support staff as well as a change in the mindset of clinical staff on the floor. It will almost inevitably require international support of some kind for low- and middle-income country researchers, be it university programs or other academic or private institutions. Success can occur; most likely it will occur by partnering with local research experts outside of emergency medicine in some combination with international networks and mentoring. Perhaps the most critical elements to success are intellectual curiosity and a burning flame of passion - and neither of those carry a financial cost.
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 | 0.247 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.010 | 0.072 |
| Scholarly communication | 0.032 | 0.054 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.019 | 0.043 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".