Introducing FIRSTmed-ADLX Model: As a New Learner Experience LX Model in the Medical Context, Public Health and Wellness Education Intervention - the Case of the IMC Wellness Office in KSA
Bibliographic record
Abstract
The field of health and wellness education in the public health presents unique challenges due to the need for effective frameworks that can help educators and healthcare providers facilitate the transfer of wellness concepts to clients for behavior modification and transformation for better health outcomes (Solhi et.al, 2020; Olsen, 2010). In response to this need, the FIRSTmed-ADLX (Focusing – Interacting – Reviewing – Sequencing – Transforming) (Active Deep learner eXperience) was developed as medical education, public health, and wellness sub-context of the successful FIRST-ADLX framework (Bahgat et al. 2018), This paper explores the compatibility and effectiveness of the model for public health and wellness education. The study adopts an exploratory mixed research method that combines qualitative by the template analysis technique using MAXQDA and quantitative for descriptive analysis using MS Excel.Findings include the unprecedented paradigm transformation expressed by healthcare providers and medical educators in their understanding of education and learning principles. The study revealed that participants' deep engagement and motivation, coupled with their perception of themselves as learners, greatly assisted them in enhancing their design and facilitation capabilities by applying the domains and principles of the model Wellness clients exhibited notable transformations in their learning experiences and personal behaviors, particularly in adopting healthier lifestyles and embracing wellness practices.Furthermore, the analysis has brought to light the notable impact of FIRSTmed-ADLX on the working culture within the IMC's (International Medical Center) wellness office results. This encompasses processes, internal language, and behavioral shifts, underscoring the substantial impact of the model.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".