Exploratory Study of Rural Physicians' Self-Directed Learning Experiences in a Digital Age
Bibliographic record
Abstract
INTRODUCTION: The nature and characteristics of self-directed learning (SDL) by physicians has been transformed with the growth in digital, social, and mobile technologies (DSMTs). Although these technologies present opportunities for greater "just-in-time" information seeking, there are issues for ensuring effective and efficient usage to compliment one's repertoire for continuous learning. The purpose of this study was to explore the SDL experiences of rural physicians and the potential of DSMTs for supporting their continuing professional development (CPD). METHODS: Semistructured interviews were conducted with a purposive sample of rural physicians. Interview data were transcribed verbatim and analyzed using NVivo analytical software and thematic analysis. RESULTS: Fourteen (N = 14) interviews were conducted and key thematic categories that emerged included key triggers, methods of undertaking SDL, barriers, and supports. Methods and resources for undertaking SDL have evolved considerably, and rural physicians report greater usage of mobile phones, tablets, and laptop computers for updating their knowledge and skills and in responding to patient questions/problems. Mobile technologies, and some social media, can serve as "triggers" in instigating SDL and a greater usage of DSMTs, particularly at "point of care," may result in higher levels of SDL. Social media is met with some scrutiny and ambivalence, mainly because of the "credibility" of information and risks associated with digital professionalism. DISCUSSION: DSMTs are growing in popularity as a key resource to support SDL for rural physicians. Mobile technologies are enabling greater "point-of-care" learning and more efficient information seeking. Effective use of DSMTs for SDL has implications for enhancing just-in-time learning and quality of care. Increasing use of DSMTs and their new effect on SDL raises the need for reflection on conceptualizations of the SDL process. The "digital age" has implications for our CPD credit systems and the roles of CPD providers in supporting SDL using DSMTs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".