Factors influencing sustainability of online platforms for professionals: a mixed-method study in OECD countries
Bibliographic record
Abstract
Online platforms can support health and educational professionals in their daily work; however, it is challenging to keep online platforms sustainable. This paper aims to indicate the most important factors of platform sustainability from the perspective of professionals involved in online platforms. Further, it aims to understand how these factors operate. A mixed methods study was carried out among professionals from Europe, Australia, the USA and Canada. In the first phase, the importance of 54 factors from the literature was assessed with a questionnaire among 17 professionals. The relative importance of the factors and the consensus regarding this importance were calculated using median scores and interquartile deviations. In total, 19 factors were selected representing general characteristics, characteristics related to the platform, communication, visitor and context. In the second phase, insight was gained regarding the experiences with those factors through 12 individual Skype interviews. The most frequently mentioned important factors of platform sustainability were (i) having sufficient time, resources and expertise, (ii) user friendliness and (iii) creating a sense of belonging. Platforms should use a planned approach to address a combination of factors directly from platform development. Gaining long-term resources is challenging and should be considered from the start of a project by building partnerships. To promote user friendliness, platforms should be simple, have a clear set-up and provide high-quality tools. Finally, establishing a sense of belonging could be supported by branding and face-to-face networking activities. For all aspects, involving visitors and stakeholders is essential.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".