Theories and Models in Health Sciences Education – a Literature Review
Bibliographic record
Abstract
Working within a scholarship of teaching and learning (SoTL) perspective requires a rigorous approach based on conceptual frameworks in order to build on previous developments. Nevertheless, in health sciences education, the development, implementation, and evaluation of many educational innovations are carried out without an underlying conceptual framework, partly due to a lack of knowledge about any such applicable framework. The objective of this research was to catalogue conceptual frameworks mentioned in recently published health sciences education articles and to classify them according to their use in various SoTL contexts. A literature review in health sciences education from the January, 2011 to March, 2016 period was carried out using the Pubmed, CINAHL, Embase, ERIC, and PsychINFO databases and based on the following terms: (a) theories and models; (b) education; and (c) health professionals. The titles and abstracts of articles were reviewed for purposes of including research articles, innovation reports, and synthesis articles using or discussing theories or models. Data extraction followed the SoTL classification contexts provided by Simpson et al. (2007). A total of 471 articles were selected, retrieving 324 conceptual theories and models, classified according to Simpson’s classification in one or more categories: Teaching (n=294), Curriculum development (n=182), Mentoring (n=12), Leadership/administration (n=16), and Learner assessment (n=78). In conclusion, this literature review identified conceptual theories and models mentioned in articles published in health sciences education from 2011 to 2016. This repertory highlights the importance of conceptual frameworks in health science education. It should encourage faculty members to work from a SoTL perspective by making it easier to identify conceptual frameworks pertaining to the educational innovations they are addressing.
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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.025 | 0.028 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".