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Record W2107412195 · doi:10.1136/ebn1090

Different attitudes towards mental health revealed in a survey of nurses across five European countries; more positive attitudes found in Portugal, in women and in those in senior roles

2010· letter· en· W2107412195 on OpenAlexaboutno aff
Mark Haddad

Bibliographic record

VenueEvidence-Based Nursing · 2010
Typeletter
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineWeb of scienceMental healthPsychiatryFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

Commentary on: Chambers M, Guise V, Välimäki M, et al. Nurses' attitudes to mental illness: a comparison of a sample of nurses from five European countries. Int J Nurs Stud 2010;47:350–62.[OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Examining the attitudes of nurses is a well-trod route for nurse researchers: such papers are common in our journals, describing nurses' perspectives on topics from complementary medicine to assisted suicide. These studies may involve focus groups, interviews or non-validated question sets; alternatively they may be scale development studies or may use validated scales to examine attitudes in a particular area. The paper by Chambers and colleagues is an example of the latter: it uses the Community Attitudes to Mental Illness questionnaire (developed in Canada in the late 1970s in response to deinstitutionalisation) to identify the attitudes of nurses working in mental health inpatient and community settings in five European countries. Measuring attitudes … [1]: {openurl}?query=rft.jtitle%253DInternational%2Bjournal%2Bof%2Bnursing%2Bstudies%26rft.stitle%253DInt%2BJ%2BNurs%2BStud%26rft.aulast%253DChambers%26rft.auinit1%253DM.%26rft.volume%253D47%26rft.issue%253D3%26rft.spage%253D350%26rft.epage%253D362%26rft.atitle%253DNurses%2527%2Battitudes%2Bto%2Bmental%2Billness%253A%2Ba%2Bcomparison%2Bof%2Ba%2Bsample%2Bof%2Bnurses%2Bfrom%2Bfive%2BEuropean%2Bcountries.%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.ijnurstu.2009.08.008%26rft_id%253Dinfo%253Apmid%252F19804882%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1016/j.ijnurstu.2009.08.008&link_type=DOI [3]: /lookup/external-ref?access_num=19804882&link_type=MED&atom=%2Febnurs%2F13%2F4%2F115.atom [4]: /lookup/external-ref?access_num=000275611400011&link_type=ISI

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)0.012
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.008
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 head0.076
GPT teacher head0.465
Teacher spread0.389 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2010
Admission routes1
Has abstractyes

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