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Record W1980843271 · doi:10.1097/bsd.0b013e3180471bdc

An Evaluation of Low Back-pain–related Content in Canadian Newspaper Media

2008· article· en· W1980843271 on OpenAlexaffabout
Douglas P. Gross, Jasmine Field, Kurt Shanski, Robert Ferrari

Bibliographic record

VenueJournal of Spinal Disorders & Techniques · 2008
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNewspaperMedicineRest (music)Back painLow back painBalance (ability)Physical therapyAlternative medicineAdvertisingInternal medicine

Abstract

fetched live from OpenAlex

STUDY DESIGN: Review of newspaper articles. OBJECTIVE: To assess the content of newspaper articles in 2 provinces in Canada to determine if rest or avoidance of activity is being recommended for back pain. SUMMARY OF BACKGROUND DATA: Inaccurate back pain beliefs in the general public may arise due to messages in the mass media. One persisting belief in Canada is that rest or activity avoidance is needed until back pain resolves. METHODS: We searched newspapers in 2 Canadian provinces via an electronic database for articles discussing back pain. Two trained raters used an article review template to indicate whether the article's main recommendation was to stay active, rest, was neutral (indicating a balance between rest and activity), or did not provide advice on level of activity during an episode of back pain. RESULTS: One hundred 29 articles were identified. The primary advice provided related to level of activity during an episode of back pain was stay active in 24% of articles, whereas no articles primarily recommended rest or avoidance of activity. Sixteen percent of articles were rated as neutral, indicating the authors suggested a balance between rest and activity. CONCLUSIONS: Back-pain-related newspaper articles do not carry messages that advocate rest or avoidance of activity, but rather highlight the importance of staying active during an episode or participating in exercise.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.410
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.029
GPT teacher head0.313
Teacher spread0.284 · 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

Citations4
Published2008
Admission routes2
Has abstractyes

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