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Record W4362508486 · doi:10.29309/tpmj/2023.30.04.7338

Cancer and immigrants: Same care, different approach.

2023· article· en· W4362508486 on OpenAlexaff
Munazza Saleem, Zuhera Khan

Bibliographic record

VenueThe Professional Medical Journal · 2023
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsPetrel Robertson Consulting (Canada)
Fundersnot available
KeywordsPsychosocialMedicineImmigrationAnxietyCancerSocial supportDepression (economics)Set (abstract data type)PopulationQuality of life (healthcare)GerontologyPsychiatryPsychotherapistNursingEnvironmental healthPsychology

Abstract

fetched live from OpenAlex

Objective: The purpose of the present literature review is to examine the psychosocial issues that emerge in the patients and their spouses upon cancer diagnosis and during its treatment. Furthermore, to search the immigrant specific supportive approaches when providing cancer care. Study Design: Systematic Literature Review. Period: 2008 to 2020. Material & Methods: Thirty-three articles, which met the pre-set criteria, were analyzed, and employed as a reference in this paper. Results: The analysis of the literature reported that depression, anxiety and low quality of life are prevalent among cancer patients and their spouses. The well-established evidence strengthened that culturally competent care, social, as well as linguistic support, are the immigrant tailored strategies that can help satisfy the need of this vulnerable population. Conclusion: The key research findings presented in the paper concludes that immigrants experiencing cancer and their spouses are more prone to acquire psychosocial issues due to their unprecedented circumstances that demand specific immigrants’ tailored approaches.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.865
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.037
GPT teacher head0.389
Teacher spread0.352 · 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.

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
Published2023
Admission routes1
Has abstractyes

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