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Exploring the experiences of youth with persistent post-concussion symptoms and their families with an interprofessional team-based assessment

2022· article· en· W6939594812 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2022
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsnot available
Fundersnot available
KeywordsConcussionContext (archaeology)Perspective (graphical)Qualitative researchRehabilitationPresentation (obstetrics)Descriptive researchHealth care

Abstract

fetched live from OpenAlex

A proportion of youth who experience concussion develop persistent or prolonged post-concussion symptoms (PPCS). Owing to the complex clinical presentation of PPCS, an interprofessional approach to care is increasingly recommended. Despite increased research in this area, there remains a dearth of evidence from the perspective of the recipients of interprofessional concussion care. The objective of this qualitative descriptive study was to explore the experiences of youth with PPCS and their parents who participated in an interdisciplinary team-based assessment (ITA) at a children’s rehabilitation hospital in Ontario, Canada. Semi-structured interviews were conducted with fifteen individuals (eight youth [8–17 years] and seven parents). Results suggest that the ITA serves as a context for meaningful therapeutic interactions whereby youth, their parents, and the interprofessional team establish and build therapeutic relationships, engage in dialogue emphasizing collaboration, prioritize the young person rather than the injury, and co-create an individualized treatment plan. Results are discussed within the broader literature in the areas of client and family-centered care, interdisciplinary assessment, and concussion management.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.072
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.004
Scholarly communication0.0030.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.123
GPT teacher head0.328
Teacher spread0.204 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2022
Admission routes1
Has abstractyes

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