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Record W4220667230 · doi:10.1111/add.15876

Commentary on Vandenberg <i>et al</i>.: The next step of developing and testing evidence‐based interventions for older adults who experience problem gambling and homelessness

2022· article· en· W4220667230 on OpenAlexaffabout
Julia Woodhall‐Melnik, Flora I. Matheson

Bibliographic record

VenueAddiction · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsSt. Michael's HospitalUniversity of New Brunswick
Fundersnot available
KeywordsPsychological interventionPovertyPsychologyPsychiatryGerontologyMedicinePolitical science

Abstract

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Future research that incorporates the lived experience of older adults, recognizes the impacts of gender, sexual identity and neurodegenerative illnesses and considers the impacts of housing interventions on gambling may point us towards the discovery and testing of evidence-based interventions for older adults who experience homelessness and problem gambling. Vandenberg et al. [1] investigate the link between gambling and homelessness in older adults aged 50 years and note that gambling in homeless populations is understudied. This is largely due to the myth that only those with the means to gamble do so, and that there is a general lack of awareness of gambling in unstably housed populations. However, studies find that gambling can predate homelessness, but also start or worsen during experiences of homelessness, as it presents an element of hope to those entrenched in poverty [2, 3]. To date, there are few studies that evaluate the effectiveness of interventions for problem gambling in homeless populations [3]. This key gap in the literature prompts us to question: (1) how we can arrive at screening and treatment interventions that prove effective for older adults who experience homelessness and problem gambling; and (2) how can methodological approaches that place value on lived experience lead us towards the development of effective interventions? Like Vandenberg et al., many scholars of problem gambling across homeless populations conclude that service providers should screen for, encourage help-seeking, and offer treatment interventions for problem gambling [4, 5]. However, an avenue unexplored by Vandenberg et al. is the importance of adapting screening tools and interventions for older adults who experience homelessness, problem gambling and neurodegenerative disease. Older adults who are homeless have a higher prevalence of dementia than those who are stably housed [6]. Further, problem gambling and dementia are both associated with frontal lobe dysfunction, which leads researchers to suggest that neurological assessments should be provided to those who begin problem gambling later in life [7, 8]. Recent research on dementia and homelessness finds a need for more investigation of the relationship to produce targeted interventions [9]. As dementia is associated with both gambling and homelessness, future researchers who seek to discover effective interventions should consider the role of dementia as a contributor to both homelessness and gambling in older adults. Research is also needed to determine whether treatment options should vary for older adults based on gender and sexual identity. Studies find that women favour different forms of gambling than men (e.g. electronic gaming machines and on-line gaming), therefore indicating a potential need to screen for and treat problem gambling in women and men differently [10, 11]. In the future, researchers could work with older adults with lived experience of homelessness and problem gambling to design and test population-specific screening tools and interventions that consider the implications of gender and sexual identity on gambling behaviours. The recommendation to improve housing stability as a mechanism to reduce gambling in homeless populations is important for all age groups. Many jurisdictions in Canada now rely upon Housing First programmes to house individuals. These programmes provide rapid access to housing without substance use or mental health treatment requirements under the premise that access to housing, a basic need, must be met before other concerns can be addressed [12]. While there remains a paucity of evidence about the use of Housing First to support people experiencing gambling concerns, what does exist suggests that access to stable housing may help to reduce gambling [13, 14]. Additional studies in this area are needed before any definitive conclusions can be drawn. In order to generate robust and population-specific interventions, future research should include older adults with lived experience of homelessness and gambling as both research participants and engaged research partners [2]. Vandenburg et al. note the lack of data from individuals with direct lived experience of homelessness and gambling as a study limitation. When used well, approaches such as peer interviewing [15], interactive World Cafe events [5] and lived experience research advisory groups [16], among others, increase participant engagement, which allows researchers to meaningfully engage lived experience throughout the research process and develop interventions that work for the people who need them. Dr. Woodhall-Melnik is funded by the Canada Research Chairs Program. Further, this review contributes to the work of ‘Community Housing Canada: Partners in Resilience’, an academic-community partnership supported by the Social Sciences and Humanities Research Council (Grant Number: 1004-2019-0002). The partnership is directed by Professor Damian Collins at the University of Alberta (host institution), in collaboration with Civida (lead community partner). Dr. Flora Matheson leads the Justice and Equity Lab located at MAP Centre for Urban Health Solutions, St. Michael's Hospital. Her research is focused on solutions to reduce social and health inequities among people experiencing problem gambling and imprisonment; solutions that are built with and for these communities. As a Sociologist, she uses a gender lens and social determinants of health approach to enact change. None. Dr. Woodhall-Melnik conceptualized and prepared the first draft of the commentary. Dr. Matheson made significant contributions to the argumentation and direction of the commentary. Both authors reviewed and edited the piece.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.166
GPT teacher head0.436
Teacher spread0.270 · 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".

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Citations0
Published2022
Admission routes2
Has abstractyes

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