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Record W2408810327 · doi:10.22605/rrh3664

Heterosexual female adolescents' decision-making about sexual intercourse and pregnancy in rural Ontario, Canada

2016· article· en· W2408810327 on OpenAlexaffabout
Paulina Ezer, Beverly Leipert, Marilyn Evans, Sandra Regan

Bibliographic record

VenueRural and Remote Health · 2016
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsWestern University
Fundersnot available
KeywordsRural areaSexual intercoursePregnancyReproductive healthPsychologyGrounded theoryDemographyMedicinePopulationQualitative researchEnvironmental healthSociologySocial science

Abstract

fetched live from OpenAlex

INTRODUCTION: Rural female adolescents experience unique circumstances to sexual health care and information as compared to urban adolescents. These circumstances are largely due to their more isolated geographical location and rural sociocultural factors. These circumstances may be contributing factors to an incidence of adolescent pregnancy that is higher in rural areas than in urban cities. Thus, this higher incidence of pregnancy may be due to the ways in which rural adolescents make decisions regarding engagement in sexual intercourse. However, the rural female adolescent sexual decision-making process has rarely, if ever, been studied, and further investigation of this process is necessary. Focusing on rural female adolescents aged 16-19 years is especially significant as this age range is used for reporting most pregnancy and birth statistics in Ontario. METHODS: Charmaz's guidelines for a constructivist grounded theory methodology were used to gain an in-depth understanding of eight Ontario rural female adolescents' decision-making process regarding sexual intercourse and pregnancy, and how they viewed rural factors and circumstances influencing this process. Research participants were obtained through initial sampling (from criteria developed prior to the study) and theoretical sampling (by collecting data that better inform the categories emerging from the data). Eight participants, aged 16-19 years, were invited to each take part in 1-2-hour individual interviews, and four of these participants were interviewed a second time to verify and elaborate on emerging constructed concepts, conceptual relationships, and the developing process. Data collection and analysis included both field notes and individual interviews in person and over the telephone. Data were analyzed for emerging themes to construct a theory to understand the participants' experiences making sexual decisions in a rural environment. RESULTS: The adolescent sexual decision-making process, Prioritizing Influences, that emerged from the analysis was a complex and non-linear process that involved prioritizing four influences within the rural context. The influences that participants of this study described as being part of their sexual decision-making process were personal values and circumstances, family values and expectations, friends' influences, and community influences. When influences coincided, they strengthened participants' sexual decisions, whereas when influences opposed each other, participants felt conflicted and prioritized the influence that had the most effect on their personal lives and future goals. Although these influences may be common to all adolescents, they impact the rural female adolescent sexual decision-making process by influencing and being influenced by geographical and sociocultural factors that make up the rural context. CONCLUSIONS: This study reveals important new and preliminary information about rural female adolescents' sexual decision-making process and factors that affect it. Findings improve understanding of how rural female adolescents make choices regarding sexual intercourse and pregnancy and can be used to guide future research projects that could facilitate effective development of sexual health promotion initiatives, inform rural health policy and practices, and enhance existing sexual education programs in rural communities.

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)0.001
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 score0.585
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.036
GPT teacher head0.372
Teacher spread0.337 · 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
Published2016
Admission routes2
Has abstractyes

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