Interpretive description as a qualitative research framework in speech-language pathology: A scoping review
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.
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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 | 0.306 | 0.371 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.033 | 0.031 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".