Narrative Retell Assessment Using “Frog” Stories: A Practice-Based Research Speech-Language Pathology Partnership Exploring Story Equivalency
Bibliographic record
Abstract
PURPOSE: Narrative abilities are an important part of everyday conversation, playing a key role in academic settings, at home, and in social interactions. As narrative assessments are an effective method for identifying children falling below age expectations, it has been recommended they be included as a routine part of clinical language assessments. It is important that assessments meet the needs of clinicians and their practice. The current study is a practice-based research partnership, where research questions arose from a partnership with school-based speech-language pathologists (SLPs). Working together, SLPs and researchers evaluated a bespoke narrative retell assessment tool. The current study examined recall of events in two wordless picture books, in order to evaluate story equivalency and determine if the tool was appropriate for progress monitoring. These findings were then used to develop local norms. METHOD: , followed by answering 10 comprehension questions related to story events. RESULTS: A significant effect of story was found for both main and supporting events recalled, but not for total events recalled. Total events recalled were found to be predicted by grade only. An examination of percent events recalled revealed four main and four supporting events in each story that were potentially misclassified. Reanalysis following reallocation revealed no significant effect of story for main or supporting events recalled. Normative values for each grade were created using percentile ranks of total events recalled. CONCLUSION: Through a practice-based research partnership, researchers and clinicians worked collaboratively to evaluate a tool, adapt its use, and improve evidence-based practice in a manner that was appropriate and met the needs for the clinical context.
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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 | 0.061 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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 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".