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Record W4376130427 · doi:10.2196/preprints.48400

Fear of Reprisal and Change Agency in the Public Health and Social Service System: Protocol for a Sequential Mixed Methods Study (Preprint)

2023· preprint· en· W4376130427 on OpenAlexaboutno aff
Annie Carrier, François Bolduc, Nathalie Delli-Colli, Finn Makela, Anne Hudon, Marie‐Ève Caty, Arnaud Duhoux, Michael F. Beaudoin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsnot available
Fundersnot available
KeywordsWrongdoingPublic relationsPsychologySocial workPolitical scienceLaw

Abstract

fetched live from OpenAlex

<sec> <title>BACKGROUND</title> Since they are key witnesses to the systemic difficulties and social inequities experienced by vulnerable patients, health and social service (HSS) professionals and clinical managers must act as change agents. Using their expertise to achieve greater social justice, change agents employ a wide range of actions that span a continuum from the clinical (microsystem) to the societal (macrosystem) sphere and involve actors inside and outside the HSS system. Typically, however, clinical professionals and managers act in a circumscribed manner, that is, within the clinical sphere and with patients and colleagues. Among the hypotheses explaining this reduced scope of action is the fear of reprisal. Little is known about the prevalence of this fear and its complex dynamics. </sec> <sec> <title>OBJECTIVE</title> The overall aim is to gain a better understanding of the complex dynamic process leading to clinical professionals’ and managers’ fear of reprisal in their change agent actions and senior administrators’ and managers’ determination of wrongdoing. The objectives are (1) to estimate the prevalence of fear of reprisal among clinical professionals and managers; (2) to identify the factors involved in (a) the emergence of this fear among clinical professionals and managers, and (b) the determination of wrongdoing by senior administrators and managers; (3) to describe the process of emergence of (a) the fear of reprisal among clinical professionals and managers, and (b) the determination of wrongdoing by senior administrators and managers; and (4) to document the legal and ethical issues associated with the factors identified (objective 2) and the processes described (objective 3). </sec> <sec> <title>METHODS</title> Based on the Exit, Voice, Loyalty, Neglect model, a 3-part sequential mixed methods design will include (1) a web-based survey (objective 1), (2) a qualitative grounded theory design (objectives 2 and 3), and (3) legal and ethical analysis (objective 4). Survey: 77,794 clinical professionals or clinical managers working in the Québec public HSS system will be contacted via email. Data will be analyzed using descriptive statistics. Grounded theory design: for each of the 3 types of participants (clinical professionals, clinical managers, and senior administrators and managers), a theoretical sample of 15 to 30 people will be selected via various strategies. Data will be independently analyzed using constant comparison process. Legal and ethical analysis: situations described by participants will be analyzed using, respectively, applicable legislation and jurisprudence and 2 ethical models. </sec> <sec> <title>RESULTS</title> This ongoing study began in June 2022 and is scheduled for completion by March 2027. </sec> <sec> <title>CONCLUSIONS</title> Instead of acting, fear of reprisal could induce clinical professionals to tolerate situations that run counter to their social justice values. To ensure they use their capacities for serving a population that is or could become vulnerable, it is important to know the prevalence of the fear of reprisal and gain a better understanding of its complex dynamics. </sec> <sec> <title>INTERNATIONAL REGISTERED REPORT</title> PRR1-10.2196/48400 </sec>

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)0.034
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.170
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0340.003
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 science0.0000.002
Research integrity0.0010.002
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 head0.532
GPT teacher head0.611
Teacher spread0.079 · 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.

Study designQualitative
Domainnot available
GenreProtocol

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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