Readiness of Health Care Professionals in Singapore to Teach Online and Their Technology-Related Teaching Needs: Quantitative Cross-sectional Pilot Study
Bibliographic record
Abstract
BACKGROUND: With the increasing acceptance of face-to-face classes transitioning to web-based learning due to COVID-19, there is an increasing need to have educators trained and equipped to teach online. The ability to teach in-person may not necessarily mean that one is ready teach in a web-based environment. OBJECTIVE: The objective of our study was to investigate the readiness of health care professionals in Singapore to teach online and their technology-related teaching needs. METHODS: This was a quantitative cross-sectional pilot study conducted among health care administrative staff and professionals in medicine, nursing, allied health, and dentistry. Participants were recruited via an open invitation email to all staff members of Singapore's largest group of health care institutions. Data were collected using a web-based questionnaire. Differences in the readiness of the professionals to teach online were analyzed using analysis of variance, and a 1-sided independent sample t test was performed to analyze the differences between respondents younger than 40 years and those older than 41 years. RESULTS: A total of 169 responses was analyzed. Full-time academic faculty members scored the highest for readiness to teach online (2.97), followed by nursing professionals (2.91), medicine professionals (2.88), administrative staff members (2.83), and allied health professionals (2.76). However, there was no statistically significant difference (P=.77) among all the respondents in their readiness to teach online. There was an agreement among all professionals in their need for software tools to teach; in particular, there was a significant difference in the software needs among the professionals for streaming videos (P=.01). There was no statistically significant difference in the readiness to teach online between those younger than 40 years and those older than 41 years (P=.48). CONCLUSIONS: Our study shows that there are still some gaps in terms of readiness to teach online among health care professionals. Our findings can be used by policy makers and faculty developers to identify opportunities for development among their educators so that they are ready to teach online with the appropriate software tools.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".