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Record W4387413473 · doi:10.4103/ed.ed_1_23

Wind turbines: Vacated/abandoned homes – Exploring research participants’ descriptions of adverse health effects and medical diagnoses provided by their physicians and physician specialists

2023· article· en· W4387413473 on OpenAlexaff
Carmen Krogh, Robert Y. McMurtry, William B Johnson, Mariana Alves‐Pereira, Jerry L. Punch, Anne Dumbrille, Debra Hughes, Linda Rogers, R. Rand, Lorrie Gillis

Bibliographic record

VenueEnvironmental Disease · 2023
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsWestern University
Fundersnot available
KeywordsMedical diagnosisHarmGovernment (linguistics)Health careQualitative researchFamily medicineMedical recordPsychologyMedicinePublic relationsMedical educationSociologyPolitical scienceSocial psychologyPathology

Abstract

fetched live from OpenAlex

Introduction: The risk of harm associated with living within 10 km of industrial wind turbines (IWTs) is unresolved and continues to be debated internationally. While sources such as judicial proceedings, scientific literature, social media, and Internet websites report that some neighbors contemplate leaving their homes, research on this topic is limited. This study continues to explore why they contemplated such a housing decision. Methodology: The ethics-reviewed study used the qualitative Grounded Theory (GT) methodology and interviewed 67 consenting participants, 18 years or older, who had previously lived, or were currently living, within 10 km of IWTs. Audio files were transcribed to text and the data were coded and analyzed using NVivo Pro (v. 12.6) software. Objectives: The objective of this manuscript is to explore participants’ descriptions of their medical diagnoses provided by their physicians and physician specialists. Results: Data analysis revealed primary and subthemes associated with environmental interference and altered living conditions. Of the 67 participants, eight described their diagnoses of medical conditions as given by their physicians and physician specialists. Descriptions of conversations with participants’ health-care providers were also surveyed. Discussion: Medical diagnoses, descriptions of comments by health practitioners and the commonality of globally reported adverse health effects (AHEs), support the potential risk of locating IWTs near residential areas. It is recommended that members of the public, government authorities, policy makers, researchers, health practitioners, and social scientists with an interest in health policy and disease prevention acknowledge this risk and advocate for the immediate, effective, and timely resolution for affected neighbors. Conclusions: The GT methodology was used to develop a substantive theory regarding the housing decisions of participants living within 10 km of a Wind Power Plant. Results from the interviews indicate that these decisions were motivated by the potential for, or the experience of, AHEs which they attributed to living in proximity to these installations.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.093
GPT teacher head0.387
Teacher spread0.295 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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