eHealth Use Among First-Generation Immigrants From Pakistan in the Oslo Area, Norway, With Focus on Diabetes: Survey Protocol
Bibliographic record
Abstract
BACKGROUND: A variety of eHealth services are available and commonly used by the general public. eHealth has the potential to engage and empower people with managing their health. The prerequisite is, however, that eHealth services are adapted to the sociocultural heterogeneity of the user base and are available in a language and with contents that fit the users' preference, skills, and abilities. Pakistani immigrants in the Oslo area, Norway, have a much higher risk of Type-2 diabetes (T2D) than their Norwegian counterparts do. In spite of having access to information and communication technology (ICT) and the Internet, ICT skills in this population are reported to be relatively low. Further, there is insufficient information about their use of and attitudes toward eHealth services, necessitating investigation of this group in particular. OBJECTIVE: This study targets first-generation immigrants from Pakistan living in the Oslo area and examines their use of and attitudes toward eHealth services, specifically: information searches, communication using ICT, and use of ICT for self-management or decision making, all concerning T2D. METHODS: Due to a high prevalence of low literacy among the target population, we employed questionnaire-based individual interviews. The questionnaire was developed by implementing potentially relevant theoretical constructs (technology acceptance model (TAM) and health belief model (HBM)) as measures. To explore issues around language, culture, and general ICT skills, we also implemented questions that we assume were particularly relevant in the context studied but do not appear in any theoretical frameworks. The questionnaire was revised to reflect results of a pilot study involving 10 participants. We employed culturally sensitive sampling methods to reach informants who could otherwise fail to be included in the survey. RESULTS: This paper presents a survey protocol. The data collection is ongoing. The aim is to collect 200 responses in total by March 2016. CONCLUSIONS: For eHealth to become an influential social innovation, equal access to eHealth services regardless of users' language, culture, and ICT skills is a prerequisite. Results from this study will be of importance for understanding how people who may not maximally benefit from eHealth services today could be targeted in the future.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".