Have the Health Care Professionals Needed Training Competencies to Educate Their Clients?
Bibliographic record
Abstract
INTRODUCTION: Health care professional are the first and most important level of health care providers that their training competencies determines the health of society. This study aimed to determine the training competencies of health care professionals for training the clients. MATERIALS & METHODS: This is a descriptive cross sectional study conducted in Mashhad’s health care centers in 2013 using probability stratified cluster sampling technique. A total of 250 heath care professionals in the departments of vaccination, mothers and children care, disease fighting, midwifery and environmental health participated in the study. The research instrument was a check list to observe the training performance of the health care professionals. Its validity confirmed by the content validity. Its reliability calculated through inter-rater agreement with a correlation coefficient (r=0.70).The data were analyzed using descriptive statistics and analytical tests including Pearson correlation test, independent sample T-test, ANOVA, and Chi square. RESULTS: The results showed that most health care professionals (66.4%) had the proper training competencies in client education. Training competencies were dependent on individual characteristics such as marital status, work place, employment status, age, experience, and history of participation in different training health workshops. The results showed that the training competencies of 166 workers (66.4%) were at good and acceptable level (13.21±1.79). There was a significant difference between training competencies of the health care professionals and their individual characteristics such as marital status, work place, employment status, and the experience of participating in training workshops (p<0.05). CONCLUSION: Based on the results, although the training competencies of health care professionals is an appropriate level, but with considering of this importance for training competencies in promotion of community health, it is necessitate to take actions to upgrade them to excellence level. Appropriate training performance is the most important strategy in preliminary health care. In the present study, the training competencies of the health care professionals and the factors influencing them were determined. Planning for promoting both training and assessment of the health care professionals apart from their general performances is vital.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".