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Record W2205486429

A critical exploration of voluntary sector social policy advocacy with marginalized communities using a population health lens and social justice.

2008· dissertation· en· W2205486429 on OpenAlexfundno aff
Gloria DeSantis

Bibliographic record

VenueoURspace (University of Regina) · 2008
Typedissertation
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
FundersUniversity of TorontoFondations communautaires du CanadaQueen's UniversityVictoria UniversityVictoria University of WellingtonOxfam AmericaUniversity of OxfordWilfrid Laurier UniversityMcGill UniversityUniversity of Regina
KeywordsSocial justiceVoluntary sectorThrough-the-lens meteringPolitical sciencePopulationTurnoverSocial policyLens (geology)Public administrationSociologyPublic relationsCriminologyLawEconomicsManagementEngineering
DOInot available

Abstract

fetched live from OpenAlex

There appears to be little data on the social policy advocacy work of the voluntary social service sector, also known as community-based organizations (CBOs), in Canada and their role in helping to create healthier communities. Research on this topic is timely in light of the following: shifting expectations of social service CBOs over the past few decades; questions about CBO-government relations; a growing importance of measuring the outcomes/impacts of the social service CBO sector; a need to alter free market ideology and introduce the counterweight of social justice principles to reduce health inequities; a growing interest in holistic policy development; growing awareness that social policies have health implications; and the Canadian welfare state is under transformation. My purpose was twofold: to explore the evolving nature of policy advocacy work undertaken by social service CBOs in Saskatchewan using a population health lens, and to examine the perceived outcomes/impacts of these processes on marginalized groups of people, CBOs, governments and communities using this lens. Using a critical inquiry methodology, qualitative data were collected through a multi-method approach. A case study design was adopted. An examination of the case study context comprised data collected through telephone interviews with 39 voluntary social service agencies from 18 communities throughout Saskatchewan, through government annual reports spanning 60 years and through observations of the political context. The case study included an examination of documents from a policy advocacy coalition, personal interviews with 17 ii individuals involved with the coalition, and observations of the coalition. Follow-up focus groups were conducted with these 17 interviewees. There were a number of findings. There has been growth in the number and diversity of social service CBOs over the past 30 years, government funding cuts and Canada Revenue Agency rules negatively affected CBOs, CBOs perceive policy advocacy is interconnected with other advocacy types, a sense of fear and vulnerability affect some advocacy participants, and of the 39 social service CBOs, 35 said they believed they contribute to people’s health/well-being through their daily work with the social determinants of health (e.g., poverty). A number of different types of advocacy processes were found to exist and some included marginalized people while others did not; CBOs’ choices about including people appeared to depend on a number of conditions (e.g., perception of participation barriers, sense of vulnerability). Interviewees described a variety of perceived outcomes/impacts of advocacy processes (e.g., learning, behaviour change, social connectedness, emotional reactions) in different spheres. The advocacy processes and their impacts were multiple, fluid and not totally predictable. A conceptualization of policy advocacy processes and population health was formulated as were implications and suggested actions for moving toward the creation of healthier communities through enhanced engagement in social policy making.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.114
GPT teacher head0.391
Teacher spread0.277 · 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 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

Citations5
Published2008
Admission routes1
Has abstractyes

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