MétaCan
Menu
Back to cohort
Record W2957765577 · doi:10.26911/theicph.2019.01.45

The Impact of Family and Peer Supports in Reducing Depression among Osteoarthritis Patients

2019· article· en· W2957765577 on OpenAlexaboutno aff
Wahyu Tri Sudaryanto, Ambar Mudigdo, Bhisma Murti

Bibliographic record

VenuePromoting Population Mental Health and Well-Being · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMental Health and Well-being
Canadian institutionsnot available
Fundersnot available
KeywordsDepression (economics)OsteoarthritisMedicinePhysical therapyWOMACPeer supportFamily supportBeck Depression InventorySocial supportAnxietyInternal medicinePsychiatryPsychologyAlternative medicine

Abstract

fetched live from OpenAlex

Background: Previous studies found that anxiety or depression were highly prevalent among osteoarthritis (OA) patients. This study aimed to examine the impact of family and peer support in reducing depression among OA patients. Subjects and Method: A case-control study was conducted at Dr. Moewardi Hospital and Dr. Soeharso Orthopedic Hospital, Surakarta, Central Java, from January to February 2018. A sample of 200 OA patients was selected by simple random sampling. The dependent variables were depression. The independent variables were pain level, functional disability, family support, and peer support. Data on depression were measured by Beck's Depression Inventory (BDI). Functional disability data were measured by The Western Ontario and McMaster University Arthritis Index (WOMAC). The other variables were collected by questionnaire. The data were analyzed by path analysis, run on Stata 13. Results: Family support (b= -0.75; 95% CI= -1.39 to -0.11; p= 0.022) and peer support (b= -1.25; 95% CI= -1.90 to -0.59; p<0.001) reduced depression in OA patients. Pain level was indirectly and positively associated with depression (b= 1.54; 95% CI= 0.88 to 2.20; p<0.001) through functional disability. Conclusion: Family support and peer support reduce depression in OA patients.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.008
GPT teacher head0.320
Teacher spread0.312 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venuePromoting Population Mental Health and Well-BeingSame topicMental Health and Well-beingFrench-language works237,207