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Record W2767419341 · doi:10.1177/1074840717738224

Sustaining Engagement in Longitudinal Research With Vulnerable Families: A Mixed-Methods Study of Attrition

2017· article· en· W2767419341 on OpenAlexaffabout
Carla Ginn, Muhammad Kashif Mughal, Hafsa Syed, Amanda Rae Storteboom, Karen Benzies

Bibliographic record

VenueJournal of Family Nursing · 2017
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAttritionPsychologyLongitudinal studyEnvironmental healthMedicine

Abstract

fetched live from OpenAlex

The aim of this mixed-methods study was to investigate attrition at the age 10-year follow-up in a study of vulnerable children and their families living with low income following a two-generation preschool program in Calgary, Alberta, Canada. Quantitative factors associated with attrition included: (a) food bank use; (b) unstable housing; (c) child welfare involvement; (d) unpartnered status; and (e) caregiver noncompletion of high school. Qualitative themes related to attrition included: (a) income and employment; (b) health; (c) unstable housing; (d) change of guardianship; (e) domestic violence; (f) work and time management challenges; and (g) negative caregiver-child relationships. Triangulation of quantitative and qualitative results occurred using Maslow's Hierarchy of Needs; families with unmet physiological, safety, belongingness and love needs, and esteem needs were more likely to attrite. Attrition in longitudinal studies with vulnerable families is complex, affected by frequently changing life circumstances, and struggles to access necessities of life. Strategies for retaining vulnerable families in longitudinal research are offered.

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)0.016
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.331
GPT teacher head0.584
Teacher spread0.253 · 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

Citations11
Published2017
Admission routes2
Has abstractyes

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