A multi-dimensional approach to measure the use of social media tools in accessing health information
Bibliographic record
Abstract
This research explores perceived user satisfaction and the impact of students using Mobile Wireless Communication Technology (MWCT) and Social Media tools (SM) for accessing health information. It was specifically concerned with whether there was a spatial pattern based on students' location or other social characteristics. An online survey was designed and utilised to collect quantitative, qualitative and spatial data. This study is unique, as it provides multi-dimensional empirical evidence (i.e., quantitative, qualitative, and spatial evidence) that underlies and complements each other. Our findings indicate that there is some evidence of a pattern as to who uses these tools more extensively for accessing health information. For example, families with kids, people who live with partners etc. Proximity to campus was not found to be correlated, and no spatial structure was found in relation to the question: Who used or did not use MWCT to access health information? Therefore, this paper argues for the inclusion and expansion of health information utilising MWCT and SM tools amongst students, which, in turn, complements traditional methods to accessing health information. The study uses a multi-dimensional approach in obtaining empirical evidence. Utilising quantitative, qualitative, and spatial analysis, our analysis instruments are interweaved and complement each other. This also provides unique robustness to this study because of the variety of evidence provided. Potentially, the findings of this paper can be used by other organisations to promote the development of new approaches and the development of online tools to encourage the access of health information by university students. This, in turn, may play a positive role in their health status.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".