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Record W2168906543 · doi:10.1136/jfprhc-2012-100486

The use of local anaesthesia for intrauterine device insertion by health professionals in the UK

2013· article· en· W2168906543 on OpenAlexaboutno aff
Hannat Akintomide, Robert D. E. Sewell, Judith Stephenson

Bibliographic record

VenueJournal of Family Planning and Reproductive Health Care · 2013
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIntrauterine deviceHealth professionalsFamily planningQuarter (Canadian coin)Health careFamily medicinePopulationNursingObstetricsGynecologyResearch methodologyEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Pain associated with the insertion of an intrauterine device (IUD) is a known barrier to intrauterine contraception use in the UK. It is good practice for health professionals to discuss pain relief and use with women prior to the insertion of an IUD. OBJECTIVES: This study aimed to determine the prevalence of and reasons for and against the use of local anaesthesia (LA) for IUD insertion. METHODS: A survey was undertaken using paper questionnaires to determine LA use for IUD insertion by UK health professionals. RESULTS: Overall, approximately one quarter (n=129) of all respondents use LA routinely, one quarter hardly ever or never use LA, while the remaining half use it sometimes. Use of LA was more prevalent among health professionals who worked in integrated sexual and reproductive health and contraception-only services, compared to general practice. UK health professionals who hardly ever or never used LA for the insertion of IUDs were more likely to be working in general practice. CONCLUSIONS: The results of this survey suggest that more UK health professionals need to routinely discuss pain relief and offer this to their patients prior to IUD insertion as part of the care pathway for patients who choose to use intrauterine contraception.

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)0.000
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 score0.830
Threshold uncertainty score0.286

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.075
GPT teacher head0.379
Teacher spread0.304 · 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

Citations7
Published2013
Admission routes1
Has abstractyes

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