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Record W4310988999 · doi:10.46919/archv3n7-007

Empowerment in chronic disease management: a mixed study approach

2022· article· en· W4310988999 on OpenAlexaff
Elisabete Luz, Fernanda Bastos, Margarida Maria Silva Vieira

Bibliographic record

VenueJournal Archives of Health · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsImpact
Fundersnot available
KeywordsEmpowermentRegimenChronic diseaseMindsetIntrusivenessMedicineDiseasePsychologyClinical psychologyIntensive care medicinePsychotherapistInternal medicineComputer science

Abstract

fetched live from OpenAlex

Aim: To explore how empowerment in chronically-ill patients could contribute to their therapeutic regimen management style. To investigate relation between chronic illness impact, therapeutic regimen management style, and the chronic disease intrusiveness´s in a person's life. Methods: A survey of 271 patients with chronic conditions administered once time with three questionnaires. From this simple (271 patients with Chronic illness.) we did nine interviews with them. Results: Regarding the socio demographic variables, age and schooling level reached statistical significance concerning individual empowerment, formally guided, and abandoned scores of the therapeutic regimen management styles. Regarding the predictive model, four multivariable linear regression models were constructed with the overall empowerment level as a dependent variable. The theoretical explanation was identified: “Facilitating decision-making according to each mindset". Conclusion: This study confirms the association between individual empowerment, the therapeutic regimen management style and the interference of chronic disease.

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)0.049
metaresearch head score (Gemma)0.032
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.258

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.006
Science and technology studies0.0020.002
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.024
GPT teacher head0.320
Teacher spread0.296 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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