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Record W4391033767 · doi:10.3389/fresc.2024.1290800

Exploring the valued outcomes of school-based speech-language therapy services: a sequential iterative design

2024· article· en· W4391033767 on OpenAlexaff
Peter Cahill, Stella Ng, Lyn S. Turkstra, Mark A. Ferro, Wenonah Campbell

Bibliographic record

VenueFrontiers in Rehabilitation Sciences · 2024
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of WaterlooUniversity of TorontoMcMaster University
Fundersnot available
KeywordsSummative assessmentContext (archaeology)Quality (philosophy)TeamworkPsychologyMedical educationQualitative researchComputer scienceService delivery frameworkService (business)Formative assessmentPedagogyMedicineSociology

Abstract

fetched live from OpenAlex

Background: Achieving outcomes that community members value is essential to high-quality, family-centred care. These valued outcomes should inform the production and interpretation of research evidence. To date, outcomes included in studies of service delivery models for speech-language services in schools have been narrowly defined, and do not match the outcomes suggested as important by families, teachers, and children. The most important outcomes of school-based, speech-languages services have not been directly and systematically investigated. We aimed to address this gap by asking school community members what outcomes were most relevant to evaluating and improving the delivery of speech-language services in schools. Methods: A sequential, iterative mixed-method study was conducted using interviews with 14 family members, educators, and speech-language therapists that asked what outcomes or impacts of school-based services they considered most important or valuable. Summative content analysis was used to analyse the data. Structural topic modelling between rounds of qualitative analysis was used to describe both the quality and the quantity of the interview content. School community members' perspectives were compared through estimation of topic proportions within interviews from each member group and through qualitative comparison. Results: Structural topic modelling diagnostics and qualitative interpretation of topic output suggested a six-topic solution. This solution was estimated successfully and yielded the following topics: (1) meeting all needs appropriately, (2) teamwork and collaboration, (3) building capacities, (4) supporting individual student needs in context, (5) coordinating care, and finally (6) supporting core educational goals. Families focused on school-based services meeting all needs appropriately and coordinating care, while educators highlighted supporting individual student needs in context. By contrast, speech-language therapists emphasized building capacities and supporting core educational goals. All school community members agreed that current assessment tools and outcome measures were inadequate to capture the most important impacts of school-based services. Conclusions: Outcomes identified by school community members as important or valuable were broad, and included individual student outcomes, interpersonal outcomes, and systems-level outcomes. Although these outcomes were discussed by all member groups, each group focused on different outcomes in the interviews, suggesting differences in the prioritization of outcomes. We recommend building consensus regarding the most important outcomes for school-based speech-language services, as well as the prioritization of outcomes for measure development.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.096
GPT teacher head0.363
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2024
Admission routes1
Has abstractyes

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