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Record W4313597999 · doi:10.3389/ijph.2022.1605296

“Hard-To-Reach” or Hardly Reaching? Critical Reflections on Engaging Diverse Residents From Low Socio-Economic Status Neighborhoods in Public Health Research

2023· article· en· W4313597999 on OpenAlexafffund
Zeinab Aliyas, Patricia Collins, Shadé Chrun-Tremblay, Tevfik Bayram, Katherine L. Frohlich

Bibliographic record

VenueInternational Journal of Public Health · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsQueen's UniversityUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsPublic healthEnvironmental healthPolitical sciencePublic relationsEconomic growthGerontologyMedicineNursingEconomics

Abstract

fetched live from OpenAlex

Keywords: questionnaire survey, low socio-economic neighbourhoods, recruiting minorities, child studies, play street, school street While socioeconomically disadvantaged populations are more likely to experience poor health, they are less likely to be represented in public health research [1][2][3].This is particularly true in the case of low SES first-generation immigrant communities who may neither speak the language of their adopted country nor be computer literate.The low representation of such communities in research is in part due to the additional strategies needed to recruit and retain these populations, leading researchers to sometimes label socioeconomically disadvantaged immigrant populations as "hard-toreach."The labeling effectively blames these populations for their lack of engagement in research and can lead to further exclusion [2,4].Ensuring their inclusion in public health research should be prioritized in order to dismantle the structures that keep them in poor health in the first place [5].In this regard, a number of studies have outlined the need for flexibility and tailoring of data collection methods but still, the methodological rigor of these studies is variable, and the body of evidence has significant gaps.Furthermore, approaches effective in one setting or one population may not be generalizable to another, which emphasizes the importance of more studies in varying communities.This paper aims to explore the importance of using tailored methods in the recruitment of socioeconomically disadvantaged populations in order to increase the representation of these groups in public health research as well as some of the caveats of these methods.The recruitment we describe took place as part of a project entitled "Levelling the Playing Fields," a population health intervention research project studying the effects of Play Street (PS) and School Street (SS) interventions on children's free play, independent mobility, and active transportation.In this paper, we present the strategies we used to mitigate barriers to participation in low SES first-generation immigrant neighborhoods as well as the methodological implications we faced.As such, we argue that methodological tailoring requires continual readjustments throughout the process of conducting research in such communities in order to make participation more accessible.Our research suggests that "hard-to-reach" populations are not "hard-to-reach" per se, but in fact reachable if conventional public health research methods are adapted to the needs of their targeted populations.From July to November 2021 we sought to recruit study participants from two socioeconomically disadvantaged neighborhoods in Montreal, Quebec, both of which contained important proportions of racialized people recently immigrated to Canada from the Global South.The target population was parent-child pairs from the neighborhoods where the SS and PS would be operating.

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)0.180
metaresearch head score (Gemma)0.179
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.820
Threshold uncertainty score0.949

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1800.179
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0590.087
Scholarly communication0.0290.034
Open science0.0070.040
Research integrity0.0250.067
Insufficient payload (model declined to judge)0.0040.001

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 head0.620
GPT teacher head0.639
Teacher spread0.019 · 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
DomainMethods
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
Published2023
Admission routes2
Has abstractyes

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