Critical Ethnography: A Useful Methodology in Conducting Health Research in Different Resource Settings
Bibliographic record
Abstract
Over the years, many policies have been implemented across nations to prevent, reduce and tighten enforcement on smoking and tobacco use. However, despite all of the major initiatives, smoking related deaths and diseases still remain high and present a major challenge for many nations of the world. In this paper we argue that conducting a critical ethnography study in different settings, as this research sets out to do (in Nigeria) is a first step to understanding the tobacco control policies that will work effectively in different resource settings. As the act of smoking becomes global, it is beneficial to study the effect of specific methods, methodology and policies in addressing smoking in the population. This paper is one of three on the study of public health challenge of smoking in Nigeria, and explains the method used in collecting and analyzing data. The research was undertaken and analyzed through a critical ethnography lens using critical realism as a philosophical underpinning. In the study we relied upon the following components: original field work in Nigeria which includes participant observation of smokers, in-depth interviews and focus groups with smokers, and in depth interviews with health professionals working in the area of tobacco control in Nigeria.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.139 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.014 | 0.025 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".