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Record W2610568568 · doi:10.1177/0046958017704608

A Critical Analysis of Debates Around Mental Health Calls in the Prehospital Setting

2017· article· en· W2610568568 on OpenAlexaff
Polly Ford-Jones, Claudia Chaufan

Bibliographic record

VenueINQUIRY The Journal of Health Care Organization Provision and Financing · 2017
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsYork University
FundersWorld Health Organization
KeywordsMental healthPsychosocialAccountabilityPublic relationsPoliticsPublic healthPsychologyHealth carePerspective (graphical)Political scienceNursingMedicineBusinessPsychiatry

Abstract

fetched live from OpenAlex

Paramedics, health care workers who assess and manage health concerns in the prehospital setting, are increasingly providing psychosocial care in response to a rise in mental health call volume. Observers have construed this fact as "misuse" of paramedic services, and proposed as solutions better triaging of patients, better mental health training of paramedics, and a greater number of community mental health services. In this commentary, we argue that despite the ostensibly well-intentioned nature of these solutions, they shift attention and accountability away from relevant public policies, as well as from broader economic, social, and political determinants of mental health, while placing responsibility on those requiring services or, at best, on the health care system. We also argue that the perspective of paramedics, who are exposed to, and interact with, individuals in their everyday environments, has the potential to inform a better, structural and critical, understanding of the factors driving the rise in psychosocial crises in the first place. Finally, we suggest that a greater engagement with the political and social determinants of mental health would lead to preventing, rather than primarily reacting to, these crises after the fact.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.020
GPT teacher head0.362
Teacher spread0.342 · 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 designObservational
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

Citations37
Published2017
Admission routes1
Has abstractyes

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