Gender digital equality in ICT interventions in health: Evidence from IDRC supported projects in developing countries
Bibliographic record
Abstract
New information and communication technologies (ICTs) such as mobile phones and the Internet are considered important instruments for advancing social and economic development throughout the world. The benefits of ICTs, however, have not been evenly distributed among individuals with different socioeconomic status. For example, few studies consider how ICTs affect men and women differently. The dearth of studies that integrate gender analysis is particularly true in the case of ICT interventions in the health sector, broadly known as e-Health. e-Health refers to the use of ICTs in different aspects of healthcare including healthcare delivery, administration, education and communication. While there is a growing focus on the potential impact of e-Health application and practices in the developing countries, little attention is given to how the technologies can address women’s health concerns or how particular interventions affect men and women differently. The objective of this paper is to explore the gender dimensions of e-Health interventions in developing countries. A select number of projects funded by Canada’s International Development Research Centre (IDRC) are systematically analyzed to draw out good practices in integrating gender analysis in e-Health research projects. We conclude by summarizing the good practices and applying them to analyze new projects to ensure gender is integrated adequately. The paper underscores that e-Health interventions in developing countries need to better articulate the social processes of inequality that affect access and use by men and women.
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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 | 0.053 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".