Bibliographic record
Abstract
Trust is a concept that we often take for granted. But as much as it serves as glue for human relationships, trust lies, albeit subtly or sometimes hidden, at the crux of all global health interventions and is critical to their success or failure.1,2 The recent Ebola pandemic in West Africa shows how distrust among the community, public health officials and government institutions can lead to a massive failure in the delivery of health care and response to a disease outbreak. Media articles and commentaries attributed the slow response to the Ebola crisis to a lack of trust on many levelsdin the government, health care systems,3 health care professionals,4 foreign health care providers, and political leaders.5 Generally, there have also been a recent decline of public trust in institutions of government, business, media and NGOs.6-8 A number of factors lead to distrust in public health, including leadership influence, lack of understanding of cultures, existing myths about health, lack of transparency, lack of accountability, and ineffective communication.2 Distrust has been cited also as a major factor in the ongoing polio pandemic.9 Northern Nigeria provides a solid case where the polio vaccination initiative struggled because community concerns and myths about immunization have been allowed to perpetuate. This is attributed to community distrust and ineffective engagement by government health officials, especially with religious leaders
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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.024 | 0.033 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".