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Record W7018312602

Deep in the shadows of loss: An exploration of grief, mourning, and intellectual disability

2023· article· en· W7018312602 on OpenAlexaboutno aff

Bibliographic record

VenueScholars Commons (Wilfrid Laurier University) · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFossil Insects in Amber
Canadian institutionsnot available
Fundersnot available
KeywordsGriefThematic analysisIntellectual disabilityPerspective (graphical)Disenfranchised griefQualitative researchGrounded theoryTraumatic grief
DOInot available

Abstract

fetched live from OpenAlex

This qualitative research project explores grief and mourning experiences of people labelled/with intellectual disabilities subsequent to the death of someone important in their lives. The primary research question was: in what ways do people labelled/with intellectual disabilities experience grief after the death of a significant person in their lives? The need for a project of this kind is grounded in the lack of research and social work practice literature related to better understanding grief, mourning, and support experiences after a death from the perspective of people labelled/with intellectual disabilities. The dual purpose of this research is to better understand (and help others understand) the complex experience of grief after a death for bereaved people labelled/with intellectual disabilities, and, from this understanding, develop ideas for how social workers (and other professionals and support people) can provide sensitive, timely, and meaningful grief support over the lengthy time where they may have a range of grief reactions. To achieve this dual purpose, the research methodology of interpretive description was used within a care ethics framework of engaging with ‘vulnerable’ populations. Individual interviews were conducted with people labelled/with intellectual disabilities (N=14) from different locations in Ontario. In addition, support people (N=4), and key informants (N=5) were also interviewed. The interviews were followed by thematic analysis of the data collected. Participants provided insights relevant to the research question and pre-existing literature on the topic that have implications for understanding the intersection of grief and disability, and for practice. The findings demonstrate that although many of the grief reactions experienced by the bereaved participants are similar to those of the neurotypical population there remain significant differences in a number of areas, such as limited choice, exclusion, significant loss histories and trauma, and gatekeepers monitoring grief expression, that have implications for practice and education. Notably, bereaved people labelled/with intellectual disabilities indicated that rather than always being only the recipients of bereavement support, they want to use their experiences to help others - their bereaved peers, family members, and paid staff. This project contributes valuable practice insights and points to the need for better education for social workers (and other professionals in developmental services) in the intersecting topics of thanatology and disability studies.\nAccessible Abstract:\n· 14 people labelled/with intellectual disabilities shared what it was like for them after someone important to them died.\n· They shared that they had many different feelings, thoughts, reactions in their bodies, and questions after the person died.\n· The people labelled/with intellectual disabilities said that it is important to have the choice of whether or not to go to a funeral or the cemetery. They do not want people to keep secrets from them. They want to hear the bad news and be supported when this news is shared.\n· Many shared that they had a lot of losses in their lives (living away from home, many staff that come and go, and people who died) and some had lots of trauma experiences too.\n· Bereaved people labelled/with intellectual disabilities told me that they want to help other bereaved people (family, friends, paid staff) because they have learned so much from their own experiences.

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 score0.056
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.045
GPT teacher head0.241
Teacher spread0.196 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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