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Record W36382531 · doi:10.1111/1467-9566.13579

(The) effects of a walking exercise program on fall-related fitness, rate of bone loss, and fall-related psychological factors in elderly women

2009· dissertation· en· W36382531 on OpenAlexfundaboutno aff
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Bibliographic record

VenueSociology of Health & Illness · 2009
Typedissertation
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsnot available
FundersInstitute of Population and Public HealthCanadian Institutes of Health Research
KeywordsGerontologyPhysical therapyPhysical medicine and rehabilitationPhysical fitnessPsychologyMedicine

Abstract

fetched live from OpenAlex

This article provides a detailed account of how surgeons perceived and used a device-procedure that caused widespread patient harm: transvaginal mesh for the treatment of pelvic floor disorders in women. Drawing from interviews with 27 surgeons in Canada, the UK, the United States and France and observations of major international medical conferences in North America and Europe between 2015 and 2018, we describe the commercially driven array of operative variations in the use of transvaginal mesh and show that surgeons' understanding of their hands-on, sensory experience with these variations is central to explaining patient harm. Surgeons often developed preferences for how to manage actual and anticipated dangers of transvaginal mesh procedures through embodied operative adjustments, but collectively the meaning of these preferences was fragmented, contested and deferred. We critically reflect on surgeons' understandings of their operative experience, including the view that such experience is not evidence. The harm in this case poses a challenge to some ways of thinking about uncertainty and errors in medical sociology, and calls for attention to a specific feature of surgical work: the extent and persistence of operative practices that elude classification as right or wrong but are still most certainly better and worse.

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)0.007
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0030.004
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 head0.427
Teacher spread0.407 · 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

Citations0
Published2009
Admission routes2
Has abstractyes

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