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Record W2599162367

Barriers to nonpharmacologic treatments for stress, anxiety, and insomnia

2010· article· en· W2599162367 on OpenAlexvenueno aff
Sibyl Anthierens, Inge Pasteels, Hilde Habraken, Pascale Steinberg, Tom Declercq, Thierry Christiaens

Bibliographic record

VenueCanadian Family Physician · 2010
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsAnxietyInsomniaMedicinePsychiatryPhysical therapy
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE To explore the attitudes of FPs toward benzodiazepine (BZD) prescribing and the perceived barriers to nonpharmacologic approaches to managing stress, anxiety, and insomnia. DESIGN A questionnaire including 32 statements about treatment of insomnia, stress, and anxiety. SETTING Local quality groups for FPs in Belgium. PARTICIPANTS A total of 948 Belgian FPs. MAIN OUTCOME MEASURES Barriers to using nonpharmacologic approaches in family practice. RESULTS We identified 3 different groups of FPs according to their attitudes about BZD prescribing. A first relatively big group of FPs (39%) were not really concerned about the risks of BZD prescribing. Those in the second group (17%) were aware of the problems associated with BZDs, but did not perceive it to be their role to use nonpharmacologic approaches in family practice. Those in the third group (44%) were concerned about BZD prescribing and found it to be a “bad solution,” but were faced with various barriers to applying nonpharmacologic approaches. Surprisingly, we found that nearly 97% of FPs thought that most people were eligible for nonpharmacologic approaches, but experienced implementation barriers at the level of the patient, the level of the FP, and the level of the health care system. CONCLUSION Using different education and behavioural-change strategies for different FP groups seems important. A large group of FPs does not find prescribing BZDs to be problematic. Sensitizing and alerting FPs to this issue remains very important.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.624
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.017
GPT teacher head0.288
Teacher spread0.271 · 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

Citations3
Published2010
Admission routes1
Has abstractyes

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