Youth Health and Wellbeing: Integration of Digital Media Technologies in Youth Work Practices in the Non-governmental Organisation Sector
Bibliographic record
Abstract
This study aims to explore the use of digital media technologies in promoting the health and well-being of young people within a youth work setting in the non-governmental organisation sector. The study adopted a qualitative approach, Purposive sampling was used to select 20 youth workers from non-governmental organisations (NGOs) operating across four provinces in South Africa: Limpopo, Gauteng, the Western Cape, and the Northern Cape. Data were collected through semi-structured interviews conducted via Microsoft Teams. The data collected were analysed using the Framework Method. The study found that youth workers use digital media technologies such as virtual sessions using platforms like Microsoft Teams, TikTok videos, and health-related messaging on WhatsApp to promote the health and wellbeing of the youth. This includes providing virtual emotional support, disseminating health-related messaging, and posting videos on TikTok with health messages and content. Significant investment in youth work, particularly in the non-governmental organisation sector, is required to optimise the use of digital media technologies to promote the youth’s health and wellbeing. Such investment comprises both financial investment and the development of skilled human capital, competent in the use of digital media technologies. In conclusion, the positive aspect of digital media technologies in promoting youth health and well-being is evident in this study. Therefore, there is a need for youth workers to receive training in the use of digital media technologies which is essential in their professional development and professionalisation of youth work in developing nations like South Africa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".