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Record W4315842326 · doi:10.53841/bpshpu.2022.31.1.40

Professional responsibilities relating to addressing discrimination and difference in health psychology therapeutic practice

2022· article· en· W4315842326 on OpenAlexaff
Sasha Cain, Kiran Kaur Bains

Bibliographic record

VenueHealth Psychology Update · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsContext (archaeology)Inclusion (mineral)PsychologyPublic relationsPrejudice (legal term)Social psychologyPolitical science

Abstract

fetched live from OpenAlex

Applied HPs work in the context of cultural and societal change. Recently this included the #BlackLivesMatter Campaign, the #metoo movement, LGBTQIA inclusion and a national pandemic; all of which have highlighted inequalities in UK society. HPs often work therapeutically with clients who experience discrimination linked to the direct and indirect consequences of health conditions, lifestyle behaviours and disabilities as well as relating to other visible and invisible differences. HCPC guidelines on discrimination require registrants to not discriminate towards others and to challenge colleagues who discriminate against others. However, responsibilities are not contextualised and do not address the personal impact of discrimination faced by the psychologist themselves (personally and professionally), which may impact the perceived safety of raising professional concerns. The British Psychological Society guidance for practitioner psychologists on discrimination does not address how to discuss difference and discrimination within therapeutic practice. Internationally there are examples of guidance that could be adapted for the UK. HPs need to take a strong affirmative position on tackling discrimination in its broadest definition across marginalised groups. Professional guidance/best practice that recognises the personal experiences of discrimination and clarifies roles and responsibilities would support us to do this.

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)0.010
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: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.545
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
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 head0.209
GPT teacher head0.573
Teacher spread0.364 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2022
Admission routes1
Has abstractyes

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