‘We have the internet in our hands’: Bangladeshi college students’ use of ICTs for health information
Bibliographic record
Abstract
BACKGROUND: Information and Communications Technologies (ICTs) which enable people to access, use and promote health information through digital technology, promise important health systems innovations which can challenge gatekeepers' control of information, through processes of disintermediation. College students, in pursuit of sexual and reproductive health (SRH) information, are particularly affected by gatekeeping as strong social and cultural norms restrict their access to information and services. This paper examines mobile phone usage for obtaining health information in Mirzapur, Bangladesh. It contrasts college students' usage with that of the general population, asks whether students are using digital technologies for health information in innovative ways, and examines how gender affects this. METHODS: This study relies on two surveys: a 2013-2014 General Survey that randomly sampled 854 households drawn from the general population and a 2015 Student Survey that randomly sampled 436 students from two Mirzapur colleges. Select focus group discussions and in-depth interviews were undertaken with students. Icddr,b's Ethical Review Board granted ethical clearance. RESULTS: The data show that Mirzapur's college students are economically relatively well positioned, more likely to own mobile and smart phones, and more aware of the internet than the general population. They are interested in health information and use phones and computers to access information. Moreover, they use digital technology to share previously-discreet information, adding value to that information and bypassing former gatekeepers. But access to health information is not entirely unfettered, affecting male and female students differently, and powerful gatekeepers, both old and new, can still control sources of information. CONCLUSION: Personal searches for SRH and the resultant online information shared through discrete, personal face-to-face discussions has some potential to challenge social norms. This is particularly so for women students, as sharing information may enable them to bypass gatekeepers and make decisions about reproduction. This suggests that digital health information seeking may be exercising a disruptive effect within the health sector. However, the extent of this disruption may depend, not on students' mobile phone usage, but on the degree to which powerful new gatekeepers are able to retain control over and market SRH information through students' peer-to-peer sharing.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".