The Distinctions Between Theory, Theoretical Framework, and Conceptual Framework
Bibliographic record
Abstract
Health professions education (HPE) researchers are regularly asked to articulate their use of theory, theoretical frameworks, and conceptual frameworks in their research. However, all too often, these words are used interchangeably or without a clear understanding of the differences between these concepts. Further problematizing this situation is the fact that theory, theoretical framework, and conceptual framework are terms that are used in different ways in different research approaches. In this article, the authors set out to clarify the meaning of these terms and to describe how they are used in 2 approaches to research commonly used in HPE: the objectivist deductive approach (from theory to data) and the subjectivist inductive approach (from data to theory). In addition to this, given that within subjectivist inductive research theory, theoretical framework, and conceptual framework can be used in different ways, they describe 3 uses that HPE researchers frequently rely on: fully inductive theory development, fully theory-informed inductive, and theory-informing inductive data analysis.
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 | 0.077 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.006 | 0.066 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.008 | 0.015 |
| 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".