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Record W2139817248

Woman physician stalked. Personal reflection and suggested approach.

2005· article· en· W2139817248 on OpenAlexaff
Donna Manca

Bibliographic record

VenuePubMed · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicStalking, Cyberstalking, and Harassment
Canadian institutionsGrey Nuns Community Hospital
Fundersnot available
KeywordsStalkingPsychological interventionReflection (computer programming)MedicinePhenomenonInternet privacyPsychologyPsychiatryComputer science
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To increase awareness of the stalking and harassing behaviour physicians sometimes encounter from patients and to explore how best to approach and address this behaviour. SOURCES OF INFORMATION: A physician's personal reflection of a stalking incident is combined with a review of the literature. Few studies have addressed this subject. MAIN MESSAGE: Any family physician could be the victim of stalking. Physicians' routines and schedules are often public knowledge because of their availability to their practices; thus they are particularly vulnerable to stalkers. We rarely think of women stalking female family physicians; however, it is likely more common than we realize. Increased awareness of this phenomenon and appropriate interventions could reduce escalation of harassing behaviour. Helpful strategies could include recognizing and addressing the behaviour early, seeking assistance, and documenting all incidents in a separate file that includes tape recordings or other material. CONCLUSION: We should explore stalking and harassing behaviour openly and become aware of the risks so that we can identify appropriate strategies to avert problems and deal with stalkers.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.934
Threshold uncertainty score0.475

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.034
GPT teacher head0.276
Teacher spread0.242 · 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 designOther design
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

Citations5
Published2005
Admission routes1
Has abstractyes

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