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Record W2274922177 · doi:10.20381/ruor-3894

Health Impact Assessment and the Inclusion of Migrants

2015· dissertation· en· W2274922177 on OpenAlexaboutno aff
Maria Benkhalti Jandu

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsInclusion (mineral)Health impact assessmentStakeholderPolitical scienceStakeholder engagementBaseline (sea)GeographyPublic relationsMedicinePsychologyNursingPublic healthSocial psychology

Abstract

fetched live from OpenAlex

There is an increasing number of international migrants worldwide and in Canada. The majority of migrants arrive with a health status higher than the average of their host country. This advantage is often lost within ten years of migration due to various reasons, most notably through the social determinants of health. These determinants are the conditions in which individuals live and work and the most relevant to migrant health include racialization, education, employment, housing, social capital, and gender. Health impact assessment (HIA) is a process with the capacity to address changes in health due to the social determinants of health by assessing the intended and unintended impacts on health that a policy, program, or project might have and recommend ways to promote positive and mitigate negative impacts. For this reason, HIA has the potential to address the observed decrease in health experienced by migrants. Various frameworks have been developed to guide the undertaking of HIA including frameworks explicitly aiming at addressing health inequities by considering particular socially disadvantaged population groups. One such example is the Health Equity Impact Assessment (HEIA) tool developed by the Ontario Ministry of Health and Long-Term Care. Although there have been a few studies addressing the inclusion of inequities in HIA in general, there has been no previous assessment of the inclusion of migrants in HIA. This doctoral thesis sought to assess the degree and way in which migrants are included in HIAs globally and across various types of HIAs and contexts. It also sought to assess the degree to which migrants were considered in local initiatives through an examination of the implementation of the Ontario HEIA tool in public health units. A scoping review of the international literature including 117 HIAs and two HIA evaluations found that only 14% of hand-searched HIAs mentioned migrants, 5% analysed migrants and only 2% included them in their recommendations. Although migrant groups were sometimes included in the process, this was seldom the case for citizens. The main reported barriers to considering migrants were a lack of available data and the significant additional resources needed. In order to undertake an evaluation throughout the province, it was first necessary to assess the way in which the Ontario HEIA had been implemented and used by public health units across the province. The scan found that nearly half of public health units had used the HEIA tool either in its original form or modified to the needs of the unit. The use of the tool was found to be influenced by the following factors: the available inputs or resources, the nature of the HIA tool, the actors and stakeholders involved, the decision-making processes within the unit or team involved, the context of the social, economic, and political environments, the nature of the project, program, or policy being assessed, and lastly, the various outputs of completing the HEIA process. Lastly, a Process and Impact evaluation assessed the way in which PHUs with a high proportion of migrants considered these migrants in their HEIAs. This study found that although migrants had been included in HEIAs, this tended to be done when the impact on migrants was anticipated. Additionally, there remains an incoherent terminology accompanied by a confounding of the concepts of migration, racialization, and ethnicity, which are reflected in the type of recommendations developed. These recommendations often focused on translation of documents into various languages and the acquiring of greater information through community partnerships. The process and capacity to include migrants in HEIAs were influenced by the availability of resources and evidence, the prioritisation of recommendations relating to migrants, and the overall impressions the staff had on the HEIA process. Nonetheless, the HEIA process was beneficial in that it strengthened relationships with migrant community organisations. This thesis work also resulted in the development of a HIA-specific theoretical framework based on the literature and empirical findings of this work. This framework is conducive to adopting a tactical approach to HIA by considering the various contextual factors influencing the completion of an HIA and implementation of its recommendations. In conclusion, although migrants are understood to be an important group often facing circumstances of disadvantage, they are only sometimes considered in HIAs. Several procedural and contextual barriers are encountered which influence their consideration. Significant guidance is still required to facilitate their adequate consideration and ensure the development of optimal recommendations. HIA frameworks should explicitly mention migrants as a potentially disadvantaged group and guidance documents could be developed to address the current gaps in understanding migrant issues.

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)0.029
metaresearch head score (Gemma)0.047
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 score0.029
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.047
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.004
Science and technology studies0.0030.004
Scholarly communication0.0060.005
Open science0.0010.010
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.012
GPT teacher head0.378
Teacher spread0.366 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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