Bibliographic record
Abstract
INTRODUCTION: Multiple methods and devices are available for the assessment of female sexual response, each with strengths and limitations that can impact interpretation of research results. As such, it is important to have an understanding of available methodologies and instruments. AIM: To review recent literature on the measurement of female sexual response, and to describe the methods and devices, and their strengths and limitations. METHODS: A literature review was performed regarding methodology and instruments used to quantify female sexual response. MAIN OUTCOME MEASURES: The description of currently available instruments and methods to quantify sexual response in women. RESULTS: Methodologies used to examine female sexual arousal employ a variety of stimuli and instruments to elicit and record sexual response. The variation in research designs across studies highlights the importance of understanding (i) how sexual response is elicited in studies; (ii) what kinds of experimental designs are available for assessing sexual psychophysiology; and (iii) the various types of instrumentation used to collect data. CONCLUSIONS: The physiological and self-reported measurement of female sexual response is crucial to our understanding of the mechanisms and factors involved with healthy sexual functioning. As such, it is important to understand the strengths and limitations associated with different stimuli, research designs, and instruments. Kukkonen TM. Devices and methods to measure female sexual arousal.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".