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Record W4388526135 · doi:10.1080/09687637.2023.2277659

Accurate yet problematic: the divided sentiments regarding brain-based addiction by professionals in the Finnish service system

2023· article· en· W4388526135 on OpenAlexaff
Nina Jokirinne, Matilda Hellman, Syaron Basnet, Petteri Koivula

Bibliographic record

VenueDrugs Education Prevention and Policy · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsAddictionPsychologyService (business)PsychiatryBusinessMarketing

Abstract

fetched live from OpenAlex

Background There is an ongoing debate regarding the value and applicability of brain-based understandings of addiction. This study examines how professionals in the Finnish addiction service system view this matter.Methods The study participants (n = 997) were recruited at different levels of policy-making, treatment, prevention work, education, administration and research. We created an online questionnaire containing both multiple-choice and open-ended questions. Quantitative and qualitative analyses were performed.Results There was a broad agreement among survey participants regarding the relevance and importance of brain-based understandings, per se. The support seemed to have increased a great deal in the past decades. On a closer view, a dichotomous attitude prevailed among the respondents: They expressed robust support for etiologies and ontologies of brain-based addiction, but simultaneously acknowledged some greater risks with neurocentrism and with wider implementations of neuroscientifically based interventions. New divisions of responsibility and the weakening of rights among concerned parties were presented as risk scenarios. The respondents feared that a medicalization of addiction would sideline social approaches.Conclusion The Finnish addiction service professionals were not prepared to let brain-based ideas of addiction guide the country’s addiction services but saw them as a useful supplementary hermeneutic and pedagogic tool.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.089
GPT teacher head0.430
Teacher spread0.341 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations2
Published2023
Admission routes1
Has abstractyes

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