Adapting an Effective Counseling Model from Patient-centered Care to Improve Motivation in Clinical Training Programs
Bibliographic record
Abstract
The value of establishing a patient-centered relationship within the context of the clinical encounter is well documented. The learner-centered method of medical education parallels the patient-centered clinical method; therefore, it should be explored as a method for teaching in the context of the learning encounter. In Japan and other Asian countries, rotations through services not related to the learner's chosen medical specialty are mandatory parts of the medical internship. Participation and effort in these rotations are often met with resistance from learners and are a common problem for medical educators. We adapted the counseling method for patients based on patient-centered methods such as motivational interviewing and solution-focused therapy to address this common problem. We show one case of a medical resident who lost his motivation to learn during his training. A resident has many kinds of mental and physical stress. One such problem arises from the gap between what they want to do and what they have to do. Strategies from motivational interviewing and solution-focused therapy were adapted to successfully resolve a common teaching problem in Japan. A physician teacher (preceptor) helped this resident solve the issue for himself instead of arguing in favor of change. The positive aspects of the counseling method were based on patient-centered medicine and proved useful and effective in counseling for medical residents. We may take the lessons learned from using patient-centered counseling methods to further develop a clear and systematic process of counseling methods for residents to conduct learner-centered medical education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".