Teleconsultations in Primary Healthcare Centres During COVID-19 Pandemic and Its Aftermath
Bibliographic record
Abstract
BACKGROUND & OBJECTIVES: Teleconsultation (TC), accounting for approximately 25% of medical consultations, is becoming increasingly pivotal in healthcare delivery. Despite its growing importance, current medical training programs often insufficiently address telecommunication skills. This study aims to augment the knowledge and proficiency of primary care physicians in TC through specialized training at Primary Healthcare Centres (PHCs) in Bahrain. METHODS: A cross-sectional study was employed to assess knowledge, practice, and training needs for TC among PHC physicians in Bahrain. Using total population sampling, data was collected via online Google survey questionnaires. The Teleconsultation Practices, Knowledge, and Training Needs Questionnaire (TPKTNQ) ensured reliability and validity, with statistical analysis performed using SPSS. Ethical approval was obtained, ensuring confidentiality and voluntary participation. RESULTS: An analysis of 185 responses from 257 distributed online questionnaires resulted in a 71.9% response rate, with 85.3% female respondents. The survey revealed that 74.9% of physicians had fair to high knowledge of TC, 89.7% practiced it, but 69% lacked formal training. Despite this, 96.7% were willing to receive updated training. Significant associations were found between TC training and practice satisfaction, knowledge levels, and further training needs, highlighting the necessity for comprehensive TC training among PHC physicians in Bahrain. IMPLICATIONS: Addressing the training gaps in TC can substantially enhance the effectiveness of telehealth services, thereby improving healthcare delivery and patient outcomes in Bahrain. Enhanced training programs can elevate care quality, optimize workload management, and increase physicians’ satisfaction, which is crucial for the retention of healthcare professionals. CONCLUSION: A significant deficiency exists in TC training among PHC physicians in Bahrain. The overwhelming willingness of physicians to engage in future training workshops underscores the readiness for adaptating TC practices. It is recommended that comprehensive training programs be implemented to bolster TC services in PHCs, thereby advancing the overall quality of patient care.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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 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".