Interpretive description as a qualitative research framework in speech-language pathology: A scoping review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Interpretive description is a constructivist, flexible, qualitative research approach used to generate knowledge to inform practice in applied disciplines. Despite potential value for the speech-language pathology profession, there has been limited discussion of interpretive description in our field to date. The purpose of this scoping review was to describe how interpretive description has been used in speech-language pathology research. We asked: a) How and to what extent has interpretive description been used as a methodological framework for primary research in the field of SLP and b) what features of interpretive description are most salient in the speech-language pathology studies that have used interpretive description to date? METHOD: Arksey and O'Malley's (2005) methodological framework for scoping reviews was used. In May 2023, we searched five databases for peer-reviewed, primary research publications that reported using ID, were specific to speech-language pathology, and were written in English. Two researchers independently reviewed articles for inclusion. A third researcher provided input when consensus could not be reached. RESULT: Nineteen articles met criteria. Data were extracted regarding article characteristics including use of theory, types of findings, clinical applicability, and description of disciplinary epistemology. CONCLUSION: Interpretive description is an emerging methodological framework in speech-language pathology research. Advantages and challenges of interpretive description for speech-language pathology are discussed.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it