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Record W3027898784 · doi:10.1111/1460-6984.12539

Perspectives of speech and language therapists in paediatric palliative care: an international exploratory study

2020· article· en· W3027898784 on OpenAlexaboutno aff
Lillian Krikheli, Shane Erickson, Lindsay B. Carey, Christa L. Carey‐Sargeant, Bernice Mathisen

Bibliographic record

VenueInternational Journal of Language & Communication Disorders · 2020
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsSnowball samplingNonprobability samplingMedicineExploratory researchFamily medicineChecklistPsychologyMedical educationNursingPopulation

Abstract

fetched live from OpenAlex

BACKGROUND: The involvement of speech and language therapists (SLTs) within paediatric palliative care (PPC) settings has been recognized within the extant literature. However, there is little understanding of SLT's specific roles and practices when working with this vulnerable cohort of children and their families. As part of a larger body of work to develop consensus-based recommendations for SLTs working in PPC, it is important to investigate demographic and caseload characteristics. AIMS: This exploratory study aimed to gather previously undocumented international demographic data pertaining to SLT service provision, caseload and training in PPC. Additionally, it sought to ascertain the current treatment and assessment approaches of SLTs, and if variations exist in beliefs and practices. METHODS & PROCEDURES: An anonymous cross-sectional survey was designed and reported according to the Checklist for Reporting Results of Internet E-Surveys (CHERRIES). The online survey consisted of 40 items spanning four domains: (1) demographic information, (2) caseload information, (3) service provision and (4) training and education. SLTs from Australia, Canada, New Zealand, the UK, Ireland and the United States were recruited using a purposive snowball sampling approach. Descriptive analysis of closed-ended survey responses and content analysis of open-ended responses are presented. OUTCOMES & RESULTS: A total of 52 respondents completed the survey. SLTs worked in a variety of PPC settings, with patients of varying age and disease groups. Over 50% of participants reported working in PPC for ≤ 4 years. Genetic disorders (34%), oncology (27%) and neurological conditions (21%) made up a significant portion of respondents' caseloads. Reported treatments and assessment approaches used by SLTs are not unique to a PPC population. Barriers and enablers for practice were identified. A portion of participants did not feel trained and prepared to assess (19.2%) or treat (15.4%) PPC clients. CONCLUSIONS & IMPLICATIONS: This study confirms that SLTs internationally have a role in the management of communication and swallowing impairments in a PPC context. However, whether current training and resources adequately support SLTs in this role remains questionable. This paper helps to provide SLTs, administrators, professional associations and tertiary institutions with foundational data to help inform workforce planning, advocacy efforts and training priorities. What this paper adds What is already known on the subject The published multidisciplinary literature has identified that SLTs have a role in PPC. However, there has been no targeted research investigating the professional characteristics of clinicians in this context, nor any detailed information regarding associated clinician beliefs or management approaches. What this paper adds to existing knowledge This study is a snapshot of attributes, practice patterns and beliefs of SLTs who work with a PPC population. It highlights SLT perspectives of education and training, as well as meta-perceptions of themselves within the multidisciplinary team. What are the potential or actual clinical implications of this work? Data presented in this paper will help to enable SLTs, organizations and associations to augment service provision and determine future professional development priorities within the field of PPC.

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)0.000
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 score0.077
Threshold uncertainty score0.597

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.355
Teacher spread0.332 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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