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Record W2794550840 · doi:10.22374/jmhan.v1i1.10

A Narrative Web-Based Study of Reasons To Go On Living after a Suicide Attempt: Positive Impacts of the Mental Health System

2017· article· en· W2794550840 on OpenAlexaffvenue
Helen Kirkpatrick, Jennifer Brasch, K. Jacky Chan, Shaminderjot Singh Kang

Bibliographic record

VenueJournal of Mental Health and Addiction Nursing · 2017
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsUniversity of OttawaSt. Joseph’s Healthcare HamiltonMcMaster University
Fundersnot available
KeywordsNarrativeMental healthPsychologyInternet privacyPsychiatryComputer scienceArtLiterature

Abstract

fetched live from OpenAlex

Background and Objective Suicide attempts are 10-20X more common than completed suicide and an important risk factor for death by suicide, yet most people who attempt suicide do not die by suicide. The process of recovering after a suicide attempt has not been well studied. The Reasons to go on Living (RTGOL) Project, a narrative web-based study, focuses on experiences of people who have attempted suicide and made the decision to go on living, a process not well studied. Narrative research is ideally suited to understanding personal experiences critical to recovery following a suicide attempt, including the transition to a state of hopefulness. Voices from people with lived experience can help us plan and conceptualize this work. This paper reports on a secondary research question of the larger study: what stories do participants tell of the positive role/impact of the mental health system. Material and Methods A website created for The RTGOL Project ( www.thereasons.ca ) enabled participants to anonymously submit a story about their suicide attempt and recovery, a process which enabled participation from a large and diverse group of participants. The only direction given was “if you have made a suicide attempt or seriously considered suicide and now want to go on living, we want to hear from you.” The unstructured narrative format allowed participants to describe their experiences in their own words, to include and emphasize what they considered important. Over 5 years, data analysis occurred in several phases over the course of the study, resulting in the identification of data that were inputted into an Excel file. This analysis used stories where participants described positive involvement with the mental health system (50 stories). Results Several participants reflected on experiences many years previous, providing the privilege of learning how their life unfolded, what made a difference. Over a five-year period, 50 of 226 stories identified positive experiences with mental health care with sufficient details to allow analysis, and are the focus of this paper. There were a range of suicidal behaviours in these 50 stories, from suicidal ideation only to medically severe suicide attempts. Most described one or more suicide attempts. Three themes identified included: 1) trust and relationship with a health care professional, 2) the role of friends and family and friends, and 3) a wide range of services. Conclusion Stories open a window into the experiences of the period after a suicide attempt. This study allowed for an understanding of how mental health professionals might help individuals who have attempted suicide write a different story, a life-affirming story. The stories that participants shared offer some understanding of “how” to provide support at a most-needed critical juncture for people as they interact with health care providers, including immediately after a suicide attempt. Results of this study reinforce that just one caring professional can make a tremendous difference to a person who has survived a suicide attempt.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.414

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 head0.018
GPT teacher head0.371
Teacher spread0.353 · 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 designQualitative
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

Citations4
Published2017
Admission routes2
Has abstractyes

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