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Record W4392870523 · doi:10.61838/kman.jprfc.1.2.1

Cultural Sensitivity in Family Research: Bridging Gaps

2023· article· en· W4392870523 on OpenAlexaff
Mehdi Rostami, Shokouh Navabinejad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsnot available
Fundersnot available
KeywordsCultural sensitivityBridging (networking)Equity (law)Culturally sensitiveHealth careFamily centered carePublic relationsSociologyPsychologyPolitical scienceSocial psychologyPsychotherapistComputer science

Abstract

fetched live from OpenAlex

We are writing to address the crucial issue of cultural sensitivity in family research and the necessity to bridge gaps and expand horizons in this area. The significance of cultural sensitivity in family research cannot be overstated, as it plays a pivotal role in ensuring the provision of effective and inclusive family-centered care. This letter aims to synthesize and integrate relevant literature to underscore the importance of cultural sensitivity in family research and to advocate for the adoption of culturally sensitive approaches in this domain. The existing literature underscores the critical importance of cultural sensitivity in family research and the provision of family-centered care. The integration of culturally sensitive approaches in family research and healthcare practices is essential to bridge gaps and expand horizons, ensuring the inclusivity and effectiveness of services provided to diverse families. It is imperative for researchers, practitioners, and policymakers to prioritize cultural sensitivity in family research and healthcare to foster an environment of inclusivity and equity.

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)0.438
metaresearch head score (Gemma)0.488
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.438
Threshold uncertainty score0.693

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4380.488
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0120.008
Science and technology studies0.0190.069
Scholarly communication0.0250.044
Open science0.0070.036
Research integrity0.0140.019
Insufficient payload (model declined to judge)0.0060.001

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 head0.305
GPT teacher head0.490
Teacher spread0.185 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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