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Record W4401460580 · doi:10.1155/2024/7549306

Analysis of the Influence of Depression on the Occupational Performance of People Diagnosed with Multiple Sclerosis and Its Impact on Caregiver Burden

2024· article· en· W4401460580 on OpenAlexaboutno aff
Sergio Rodríguez, Marta Pérez‐de‐Heredia‐Torres, David Jaraba Berné, Manuel Menéndez‐González, Rosa M. Martínez-Piédrola

Bibliographic record

VenueDepression and Anxiety · 2024
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDepression (economics)Multiple sclerosisPsychiatryPsychologyClinical psychologyMedicine

Abstract

fetched live from OpenAlex

Background: One of the most common symptoms in multiple sclerosis (MS) is depression, which causes disruption to daily participation. MS as a degenerative disease causes caregiver strain. The aim of this study is to analyze the influence of depression on the occupational performance of people with MS and to study whether these aspects influence caregiver strain. Materials and Methods: A descriptive cross-sectional observational study was carried out. A total of 124 people with a diagnosis of multiple sclerosis were assessed and administered the Beck Depression Inventory-II, the Canadian Occupational Performance Measure, and the Zarit Caregiver Burden Scale. Results: MS type influences performance, involvement, and caregiver burden. High levels of depression are associated with low levels of participation and performance. The types of MS with the highest caregiver burden are relapsing-remitting and secondary progressive. Conclusions: The type of MS negatively influences occupational performance. Depression and occupational performance are related to caregiver strain. The greater the depressive symptoms, the worse the performance, and the more caregiver strain.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.209

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.025
GPT teacher head0.300
Teacher spread0.275 · 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 designObservational
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

Citations1
Published2024
Admission routes1
Has abstractyes

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