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Refining the psychometric properties of the Trinity Student Profile - A self-report measure of occupational performance difficulties within the student role in higher education.

2024· dissertation· en· W7052213597 sur OpenAlexaff

Notice bibliographique

RevueTrinity's Access to Research Output (TARA) (Trinity College Dublin) · 2024
Typedissertation
Langueen
DomaineEngineering
ThématiquePlasma Diagnostics and Applications
Établissements canadiensTrinity College
Organismes subventionnairesnon disponible
Mots-clésRasch modelThematic analysisScale (ratio)PsychometricsFocus groupOccupational therapyItem response theoryTest (biology)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Background: The Trinity Student Profile (TSP) is a self-report measure of occupational performance difficulties within the university student role and is based on the Person-Environment-Occupation Model. The tool was developed in response to the increasing numbers of students with disabilities and mental health difficulties in university in Ireland and Classical Test Theory methodology was used to facilitate its development and piloting. The tool required further rigorous validation, and there is an increasing call for the use of person-centred measurement models such as Rasch analysis methodology to validate tools. This research aimed to refine the psychometric properties of the TSP. Methodology: A two-stage embedded design approach was used. Stage One aimed to refine the psychometric properties of the `Identifying Needs? section using Rasch analysis. Data from 667 TSP files from the disability services in Trinity College Dublin and University College Dublin was collected retrospectively and analysed using Rasch analysis. Stage Two aimed to affirm the face validity and clinical utility of the refined tool in practice. Occupational therapists from three universities engaged in an initial focus group to discuss experiences of using the 2014 version of the tool and were trained in using the refined tool. A follow-up focus group was held after trialling use of the refined tool in practice and the resulting qualitative data was analysed using thematic analysis. Results: The Rasch analysis in Stage One predominantly focused on the 6-point `Difficulty? scale used for 74-items across three item-sets (i.e., `Person? N=30; `Environment? N=20; `Occupation? N=24). The `Difficulty? scale demonstrated stronger psychometric properties as a combined item-set of occupational performance difficulties. Using this combined item-set, the 6-point scale was collapsed to a 4-point scale and 20 redundant items were removed. The 54-item 4-point scale demonstrated strong reliability, separation, and unidimensionality. An item difficulty hierarchy and paper-and-pencil keyform were developed to be used in practice. Preliminary differential item functioning analyses and outcome measurement analyses provided evidence for the tool?s generalisability and use as an outcome measure. Four themes resulted in Stage Two. The occupational therapists reported that the changes following the Rasch analysis have resulted in the tool being easier and more efficient to use in practice. However, there were issues residing in other sections of the tool that could not be remedied using Rasch analysis. Subsequently, additional refinements were made including re-branding as the Trinity Student Occupational Performance Profile (TSOPP), improving the face validity of other sections of the tool, and the development of an administration manual. Conclusion: The TSOPP is a valid and reliable self-report measure of occupational performance difficulties within the student role for students with disabilities in higher education.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,597
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0020,008
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0040,001
Intégrité de la recherche0,0000,003
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,118
Tête enseignante GPT0,386
Écart entre enseignants0,268 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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