Strategies for sustainable adoption of e-health tools for digital mental health services
Bibliographic record
Abstract
Orientation: The use of electronic (e-health) tools in digital mental health services (DMHS) at South African (SA) higher education institutions (HEIs) has rapidly increased because of the coronavirus disease 2019 (COVID-19) pandemic.Research purpose: The main purpose of this study was to evaluate how the university staff perceived the effectiveness of different strategies implemented for the sustainable adoption of e-health tools in DMHS.Motivation for the study: Despite the increasing availability of e-health tools, there is limited understanding of how university staff perceive the effectiveness of different sustainability strategies.Research approach/design and method: The study utilised a quantitative approach and surveyed 348 university staff at a SA HEI. Data analysis utilised descriptive statistics and one-sample t-tests.Main findings: The findings highlight funding, financial incentives, digital inclusion programmes and stakeholder engagement as crucial strategies for sustainable adoption. University staff emphasised the importance of training, digital health literacy campaigns, robust data privacy and security systems, and multilingual e-health services. In addition, hybrid e-health models and continuous evaluation emerged as essential strategies.Practical/managerial implications: University management should prioritise financial investments, stakeholder engagement and digital literacy programmes to improve the adoption of e-health tools. Strengthening data security, integrating hybrid service models and ensuring multilingual accessibility can further support sustainable DMHS.Contribution/value-add: This study provides evidence-based strategies for the sustainable adoption of e-health tools in SA HEIs, which thus enhance DMHS and inform policy and practice.
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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 | 0.029 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".